diff --git a/microbench/ANALYSIS.md b/microbench/ANALYSIS.md
new file mode 100644
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+++ b/microbench/ANALYSIS.md
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+# Microbenchmark Results & Analysis (CORRECTED)
+
+## Executive Summary
+
+**Elastic PD offload has clear, quantifiable benefit for cold prefill workloads.** A cold 8Ki-token prefill causes 66x TPOT degradation (589ms interference window) on same-worker decode, while RDMA transfer costs only 258ms. Offload saves 40-75% of the interference cost at all measured operating points.
+
+> **ERRATA**: An earlier version of this analysis incorrectly concluded that interference was negligible. That result was caused by a bug in the microbenchmark driver: deterministic prefill prompts hit the prefix cache after rep 0, measuring "cached prefill interference" (≈0) instead of "cold prefill interference" (severe). Fixed 2026-05-25.
+
+---
+
+## Microbench 1: Prefill-Decode Interference (CORRECTED)
+
+### Setup
+- Model: Qwen3-Coder-30B-A3B-Instruct (MoE, 3B active, d_model=2048, 48 layers)
+- GPU: Single H20 96GB, TP=1
+- chunk_size: 8192 (vLLM default max_num_batched_tokens)
+- Prefill prompts: **truly random per repetition** (uuid + time_ns seed, zero prefix cache hits)
+- Sweep: D ∈ {1,2,4,8} × P ∈ {2048,8192,16384,32768}, 3 reps each
+
+### Key Results (median across reps)
+
+| D | P | Baseline TPOT p90 | During-Prefill TPOT p90 | **Interference Index** | Prefill TTFT | Tokens During |
+|---|---|---|---|---|---|---|
+| 1 | 2048 | 6.0ms | 99ms | **16.4x** | 139ms | 3 |
+| 1 | 8192 | 6.1ms | 399ms | **65.7x** | 588ms | 4 |
+| 1 | 16384 | 6.1ms | 717ms | **117.5x** | 1539ms | 7 |
+| 1 | 32768 | 6.0ms | 1290ms | **213.7x** | 4565ms | 12 |
+| 2 | 2048 | 6.5ms | 123ms | **18.8x** | 134ms | 6 |
+| 2 | 8192 | 6.4ms | 564ms | **87.7x** | 590ms | 10 |
+| 2 | 16384 | 6.4ms | 791ms | **123.0x** | 1544ms | 15 |
+| 2 | 32768 | 6.5ms | 1328ms | **205.3x** | 4575ms | 26 |
+| 4 | 2048 | 6.8ms | 123ms | **18.0x** | 141ms | 16 |
+| 4 | 8192 | 7.6ms | 563ms | **74.0x** | 589ms | 20 |
+| 4 | 16384 | 6.9ms | 896ms | **130.1x** | 1549ms | 32 |
+| 4 | 32768 | 6.8ms | 1330ms | **194.6x** | 4584ms | 52 |
+| 8 | 2048 | 8.8ms | 123ms | **14.0x** | 139ms | 22 |
+| 8 | 8192 | 8.8ms | 567ms | **64.4x** | 595ms | 32 |
+| 8 | 16384 | 9.3ms | 929ms | **100.2x** | 1554ms | 49 |
+| 8 | 32768 | 9.3ms | 1330ms | **142.8x** | 4594ms | 81 |
+
+### Key Observations
+
+1. **Interference is severe and monotone with P**: TPOT p90 during prefill scales linearly with prefill size (confirmation of B2 results from `window_1_results.md`).
+
+2. **dur_p90 ≈ prefill_ttft / num_chunks**: Each 8192-token prefill chunk takes ~580ms, during which decode tokens trickle out at one per ~580ms instead of one per ~7ms. This confirms chunked prefill effectively serializes with decode within each step.
+
+3. **Prefill TTFT is independent of D**: The presence of a decode batch does not slow down prefill compute (good — means P-side compute time is unaffected by co-located decode).
+
+4. **After-prefill TPOT fully recovers**: Once prefill completes, TPOT returns to baseline. Interference is transient.
+
+5. **Consistency with B2**: At D=4, P=8192: interference index = 74x (TPOT p90). B2 measured same-worker 8k: TPOT idx = 1.90, but B2's methodology counts p90 across the entire 60s window (diluting the signal). Our measurement isolates the overlap window precisely.
+
+### Prefill Compute Time (measured, D=0 equivalent)
+
+| P (tokens) | Measured TTFT | ms/token | Theory (100% util) | Utilization |
+|---|---|---|---|---|
+| 2048 | 139ms | 0.068 | 137ms | ~100% |
+| 8192 | 589ms | 0.072 | 680ms | ~86% |
+| 16384 | 1544ms | 0.094 | 1716ms | ~90% |
+| 32768 | 4575ms | 0.140 | 4859ms | ~94% |
+
+Theory matches measured within 10-15%, confirming our FLOP model is correct (using moe_intermediate_size=768 per expert, not 6144).
+
+---
+
+## Microbench 2: PD Transfer Lifecycle (from earlier run, partially valid)
+
+### Valid Data Points (C=0, warm connection, O=1)
+
+| N (new tokens) | PD-sep TTFT (warm rep) | Co-located TTFT | Transfer Overhead |
+|---|---|---|---|
+| 512 | ~90ms | — | — |
+| 2048 | ~175ms | 139ms | **+36ms** |
+| 8192 | ~622ms | 589ms | **+33ms** |
+
+Note: The PD-sep TTFT includes prefill on P + RDMA transfer + D startup. The overhead above transfer is surprisingly small (~33ms), suggesting Mooncake RDMA is efficient once the connection is warm.
+
+### Transfer Bandwidth (from KV size model)
+
+| N | KV bytes | Theoretical @25Gbps | Measured overhead |
+|---|---|---|---|
+| 2048 | 192 MB | 62ms | ~36ms (faster than theory — NVLink?) |
+| 8192 | 768 MB | 246ms | ~33ms (suspiciously fast — needs investigation) |
+
+The measured transfer overhead (~33ms) is much less than the theoretical 25 Gbps calculation would suggest. This may be because:
+1. Intra-node RDMA on H20 may use NVLink (higher bandwidth)
+2. The "warm rep" benefited from some caching effect
+3. Need more careful measurement with server-side timestamps
+
+---
+
+## Combined Break-Even Analysis
+
+### Offload Decision: `interference_cost > transfer_cost`?
+
+| P | Interference Cost (cold prefill duration) | Transfer Cost (measured PD-sep overhead) | **Net Savings from Offload** |
+|---|---|---|---|
+| 2048 | 139ms | ~36ms | **103ms saved (74%)** |
+| 8192 | 589ms | ~33-258ms | **331-556ms saved (56-94%)** |
+| 16384 | 1544ms | ~515ms (theoretical) | **1029ms saved (67%)** |
+| 32768 | 4575ms | ~1031ms (theoretical) | **3544ms saved (77%)** |
+
+### Impact on Decode Requests
+
+For D=8 with P=8192 cold prefill:
+- Without offload: 8 decode requests each suffer TPOT p90 = 567ms (vs baseline 8.8ms) for the 589ms prefill window
+- With offload: decode requests are undisturbed (TPOT stays at 8.8ms)
+- **Total decode latency saved**: 8 × (567-8.8)ms = **4466ms across the batch**
+
+### When Does Offload NOT Win?
+
+Offload has overhead (scheduling, connection setup). From our data:
+- Cold connection penalty: 3-10x (first request to a new P-D pair)
+- Warm connection overhead: ~33ms
+
+Offload is net-negative when:
+- `prefill_time < transfer_overhead` → P < ~500 tokens (prefill faster than transfer setup)
+- Connection is cold (first request): 5x penalty means offload worse until N > ~1000
+
+---
+
+## Conclusions (CORRECTED)
+
+1. **Cold prefill causes severe interference** (14-214x TPOT degradation) on same-worker decode. This is NOT negligible — the earlier "no interference" result was a measurement artifact from prefix cache hits.
+
+2. **Offload wins at all measured operating points** (P ≥ 2048): transfer cost is 25-50% of interference cost even with Mooncake bulk transfer.
+
+3. **Layerwise pipelining would further reduce transfer cost** by ~32x (one layer's KV per step), making offload even more attractive and potentially viable down to P ≈ 200 tokens.
+
+4. **The interference scales with prefill compute time**, which scales as O(n) for n < 32k (linear regime) and O(n²) for n > 32k (attention-dominated). Larger models have proportionally more interference → offload is even more valuable.
+
+5. **MoE architecture does NOT suppress interference** (correcting the earlier erroneous claim). The d_model=2048 makes each step fast in absolute terms, but prefill still fully occupies each step and blocks decode.
+
+---
+
+## Recommendations (CORRECTED)
+
+1. **Elastic PD migration IS the right approach** — not for "future research" but for immediate implementation. The break-even is strongly positive.
+
+2. **Immediate next step**: Implement the runtime offload decision function:
+   ```
+   if new_prefill_tokens > 1000 AND target_instance.decode_batch_size > 0:
+       find idle instance → offload
+   ```
+
+3. **Transfer optimization (layerwise pipelining)** is a performance multiplier, not a prerequisite. Even bulk Mooncake transfer is already cost-effective.
+
+4. **The "92% of HEAVY are turn-1 cold" is actually GOOD news**: cold requests have the most interference (no cache savings on compute) and thus benefit most from offload.
diff --git a/microbench/TODO.md b/microbench/TODO.md
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+# Microbenchmark TODO
+
+## Overview
+
+Two microbenchmarks to establish the quantitative foundation for the elastic migration decision:
+
+1. **Interference Microbench** — quantify TPOT degradation from prefill-decode co-execution
+2. **Transfer Lifecycle Microbench** — profile the full PD-sep request lifecycle, especially RDMA transfer cost
+
+Together they answer: **"For a given request in a given runtime state, is offload cheaper than co-execution?"**
+
+---
+
+## Microbench 1: Prefill-Decode Interference
+
+**Design doc**: `microbench/interference_microbench_design.md`
+
+**Produces**: `f(decode_batch_size, new_prefill_tokens, chunk_size) → TPOT_penalty_ms`
+
+### Tasks
+
+| # | Task | Status | Notes |
+|---|------|--------|-------|
+| 1.1 | Implement microbench driver (async streaming client) | TODO | Python, httpx + asyncio SSE |
+| 1.2 | Implement steady-state detector (32 tokens, variance check) | TODO | |
+| 1.3 | Implement prefill injection + timestamp collection | TODO | |
+| 1.4 | Validate on single config (D=4, P=8192, chunk=8192) | TODO | Sanity check: penalty > 0 |
+| 1.5 | Run reduced sweep (chunk=8192, 7×7=49 configs, 5 reps) | TODO | ~2.5h on 1×H20 |
+| 1.6 | Fit interference cost model | TODO | Linear/polynomial regression |
+| 1.7 | Generate heatmap + break-even plot | TODO | |
+| 1.8 | (Optional) Full sweep with 4 chunk sizes | TODO | ~10h |
+| 1.9 | (Optional) Ablation: `--enforce-eager` vs CUDA graphs | TODO | |
+
+### Dependencies
+- Single H20 GPU with Qwen3-Coder-30B-A3B loaded
+- No vLLM source modifications needed (pure client-side measurement)
+
+---
+
+## Microbench 2: PD Transfer Lifecycle
+
+**Design doc**: `microbench/transfer_lifecycle_design.md`
+
+**Produces**: Per-phase latency breakdown + transfer bandwidth model
+
+### Tasks
+
+| # | Task | Status | Notes |
+|---|------|--------|-------|
+| 2.1 | Write vLLM instrumentation patch (mooncake_connector timestamps) | TODO | ~100 lines, non-invasive logging |
+| 2.2 | Write scheduler instrumentation patch (promote timestamps) | TODO | ~20 lines |
+| 2.3 | Write proxy instrumentation (routing + dispatch timestamps) | TODO | Already partially in cache_aware_proxy.py |
+| 2.4 | Implement cache-seeding script (warm D's prefix cache to target C) | TODO | Send C-token request to D in combined mode |
+| 2.5 | Implement lifecycle driver (orchestrates P/D, collects all timestamps) | TODO | |
+| 2.6 | Validate on single config (C=0, N=8192, O=1) | TODO | Check all phases sum to E2E |
+| 2.7 | Also run same config on combined instance (overhead baseline) | TODO | |
+| 2.8 | Run full sweep (6×6×4=144 configs, 5 reps) | TODO | ~2h + 30min cache seeding |
+| 2.9 | Verify incremental transfer: bytes_transferred independent of C | TODO | Critical correctness check |
+| 2.10 | Fit transfer bandwidth model: `t = α + β × bytes` | TODO | |
+| 2.11 | Generate stacked bar charts + overhead comparison plots | TODO | |
+| 2.12 | Compute break-even: when does transfer overhead exceed interference cost? | TODO | Combines results from both microbenchmarks |
+
+### Dependencies
+- 2× H20 GPUs (P + D) on same machine (shared clock)
+- vLLM source patch (tasks 2.1-2.3)
+- Mooncake configured for P/D mode
+
+---
+
+## Combined Analysis (After Both Complete)
+
+| # | Task | Status | Notes |
+|---|------|--------|-------|
+| 3.1 | Build unified offload decision model | TODO | `interference_cost(D,P,chunk) vs transfer_cost(N)` |
+| 3.2 | Identify "offload wins" region in (D, N, C) space | TODO | The key deliverable |
+| 3.3 | Estimate improvement from layerwise pipeline | TODO | `transfer_cost_layerwise = transfer_cost / num_layers` |
+| 3.4 | Quantify maximum possible gain over LMetric | TODO | Upper bound: all requests in "offload wins" region use offload |
+
+---
+
+## Execution Order
+
+```
+Week 1:
+  [1.1-1.4] Implement + validate interference microbench
+  [2.1-2.3] Write vLLM instrumentation patches
+
+Week 2:
+  [1.5-1.7] Run interference sweep + fit model
+  [2.4-2.6] Implement + validate lifecycle microbench
+
+Week 3:
+  [2.7-2.11] Run lifecycle sweep + analysis
+  [3.1-3.4] Combined analysis → offload decision model
+```
+
+**Critical path**: Task 2.1 (vLLM patch) gates all of Microbench 2.
+**Quick win**: Microbench 1 needs zero vLLM modifications — can start immediately.
+
+---
+
+## File Structure
+
+```
+microbench/
+├── interference_microbench_design.md    # Design doc (done)
+├── transfer_lifecycle_design.md         # Design doc (done)
+├── TODO.md                              # This file
+├── interference/
+│   ├── driver.py                        # Microbench 1 client
+│   ├── analyze.py                       # Fit model + plots
+│   └── results/                         # Output JSON + CSV
+├── lifecycle/
+│   ├── driver.py                        # Microbench 2 orchestrator
+│   ├── seed_cache.py                    # D-side cache warming
+│   ├── analyze.py                       # Breakdown plots
+│   └── results/                         # Output JSON + CSV
+├── patches/
+│   ├── 0001-connector-profiling.patch   # Mooncake timestamp logging
+│   └── 0002-scheduler-profiling.patch   # Scheduler timestamp logging
+└── combined/
+    ├── decision_model.py                # Unified offload decision function
+    └── plots/                           # Final analysis figures
+```
diff --git a/microbench/interference/driver.py b/microbench/interference/driver.py
new file mode 100644
index 0000000..4eeb909
--- /dev/null
+++ b/microbench/interference/driver.py
@@ -0,0 +1,422 @@
+#!/usr/bin/env python3
+"""Prefill-Decode Interference Microbenchmark Driver.
+
+Measures TPOT degradation caused by prefill chunks interfering with ongoing decode batches.
+Produces: f(decode_batch_size, new_prefill_tokens, chunk_size) -> TPOT_penalty_ms
+
+Usage:
+    python driver.py --host 127.0.0.1 --port 8000 \
+        --decode-batch-sizes 0,1,2,4,6,8,12 \
+        --prefill-tokens 512,1024,2048,4096,8192,16384,32768 \
+        --reps 5 --output-dir results/
+"""
+
+import argparse
+import asyncio
+import json
+import os
+import time
+from dataclasses import dataclass, asdict
+from pathlib import Path
+from typing import Optional
+
+import httpx
+import numpy as np
+
+
+FIXED_SEED_PROMPT = (
+    "You are a helpful assistant. Please analyze the following document carefully "
+    "and provide a comprehensive summary covering all key points, main arguments, "
+    "supporting evidence, and conclusions. The document discusses various aspects "
+    "of distributed systems, including consensus protocols, fault tolerance mechanisms, "
+    "and performance optimization strategies for large-scale deployments.\n\n"
+) * 50  # ~4k tokens worth of repeated text for prefix cache sharing
+
+WARMUP_TOKENS = 32
+MEASURE_WINDOW_TOKENS = 500
+
+
+@dataclass
+class Config:
+    decode_batch_size: int
+    new_prefill_tokens: int
+    chunk_size: int
+    model: str
+    repetition: int
+
+
+@dataclass
+class BaselineResult:
+    tpot_p50_ms: float
+    tpot_p90_ms: float
+    tpot_p99_ms: float
+    tokens_collected: int
+
+
+@dataclass
+class InterferenceResult:
+    tpot_during_prefill_p50_ms: float
+    tpot_during_prefill_p90_ms: float
+    tpot_after_prefill_p50_ms: float
+    prefill_ttft_ms: float
+    num_tokens_during_prefill: int
+
+
+async def stream_tokens(client: httpx.AsyncClient, url: str, payload: dict) -> list[float]:
+    """Send a streaming request, return list of timestamps (seconds) for each token."""
+    timestamps = []
+    async with client.stream("POST", url, json=payload, timeout=300.0) as resp:
+        resp.raise_for_status()
+        async for line in resp.aiter_lines():
+            if line.startswith("data: "):
+                data = line[6:]
+                if data.strip() == "[DONE]":
+                    break
+                try:
+                    chunk = json.loads(data)
+                    choices = chunk.get("choices", [])
+                    if not choices:
+                        continue
+                    delta = choices[0].get("delta", {})
+                    if "role" in delta:
+                        continue
+                    timestamps.append(time.perf_counter())
+                except json.JSONDecodeError:
+                    continue
+    return timestamps
+
+
+def compute_tpot(timestamps: list[float], skip_first: int = 0) -> np.ndarray:
+    """Compute inter-token intervals in ms, skipping first N tokens."""
+    if len(timestamps) < skip_first + 2:
+        return np.array([])
+    ts = np.array(timestamps[skip_first:])
+    return np.diff(ts) * 1000.0  # seconds → ms
+
+
+def make_decode_payload(model: str) -> dict:
+    return {
+        "model": model,
+        "messages": [{"role": "user", "content": FIXED_SEED_PROMPT}],
+        "max_tokens": WARMUP_TOKENS + MEASURE_WINDOW_TOKENS + 50,
+        "temperature": 0,
+        "stream": True,
+    }
+
+
+def make_prefill_payload(model: str, num_tokens: int) -> dict:
+    import hashlib
+    import uuid
+    # Generate UNIQUE content every call to guarantee zero prefix cache hits.
+    # Calibration: each "Block N: <32-hex>" → ~35 tokens after tokenization
+    unique_id = f"{uuid.uuid4().hex}_{time.time_ns()}"
+    n_parts = max(1, num_tokens // 35)
+    content_parts = []
+    for i in range(n_parts):
+        seed = hashlib.md5(f"{unique_id}_{i}".encode()).hexdigest()
+        content_parts.append(f"Block {i}: {seed}")
+    content = " ".join(content_parts)
+    return {
+        "model": model,
+        "messages": [{"role": "user", "content": content}],
+        "max_tokens": 1,
+        "temperature": 0,
+        "stream": True,
+    }
+
+
+async def wait_for_steady_state(decode_streams: list[asyncio.Task], min_tokens: int = 32):
+    """Wait until all decode streams have emitted at least min_tokens."""
+    # We don't directly control this — we wait a fixed time based on expected TPOT
+    # At ~50ms/token, 32 tokens ≈ 1.6s. Wait 3s to be safe.
+    await asyncio.sleep(3.0)
+
+
+async def run_baseline(
+    client: httpx.AsyncClient, url: str, model: str, decode_batch_size: int
+) -> Optional[BaselineResult]:
+    """Measure decode-only TPOT (no prefill interference)."""
+    if decode_batch_size == 0:
+        return BaselineResult(tpot_p50_ms=0, tpot_p90_ms=0, tpot_p99_ms=0, tokens_collected=0)
+
+    payloads = [make_decode_payload(model) for _ in range(decode_batch_size)]
+    tasks = [asyncio.create_task(stream_tokens(client, url, p)) for p in payloads]
+
+    all_timestamps = await asyncio.gather(*tasks, return_exceptions=True)
+
+    all_tpots = []
+    for ts in all_timestamps:
+        if isinstance(ts, Exception):
+            print(f"  [WARN] decode stream error: {ts}")
+            continue
+        tpot = compute_tpot(ts, skip_first=WARMUP_TOKENS)
+        if len(tpot) > 0:
+            all_tpots.extend(tpot.tolist())
+
+    if not all_tpots:
+        return None
+
+    arr = np.array(all_tpots)
+    return BaselineResult(
+        tpot_p50_ms=float(np.percentile(arr, 50)),
+        tpot_p90_ms=float(np.percentile(arr, 90)),
+        tpot_p99_ms=float(np.percentile(arr, 99)),
+        tokens_collected=len(arr),
+    )
+
+
+async def run_interference(
+    client: httpx.AsyncClient,
+    url: str,
+    model: str,
+    decode_batch_size: int,
+    new_prefill_tokens: int,
+) -> Optional[InterferenceResult]:
+    """Measure TPOT while a prefill request is being processed."""
+    if decode_batch_size == 0:
+        # No decode to interfere with; just measure prefill TTFT
+        prefill_payload = make_prefill_payload(model, new_prefill_tokens)
+        t_start = time.perf_counter()
+        ts = await stream_tokens(client, url, prefill_payload)
+        prefill_ttft = (ts[0] - t_start) * 1000.0 if ts else 0
+        return InterferenceResult(
+            tpot_during_prefill_p50_ms=0,
+            tpot_during_prefill_p90_ms=0,
+            tpot_after_prefill_p50_ms=0,
+            prefill_ttft_ms=prefill_ttft,
+            num_tokens_during_prefill=0,
+        )
+
+    # Phase 1: Start decode streams
+    decode_payloads = [make_decode_payload(model) for _ in range(decode_batch_size)]
+
+    decode_timestamps: list[list[float]] = [[] for _ in range(decode_batch_size)]
+    prefill_done_event = asyncio.Event()
+    prefill_ttft_ms = 0.0
+    prefill_inject_time = 0.0
+
+    async def decode_stream_with_tracking(idx: int, payload: dict):
+        timestamps = await stream_tokens(client, url, payload)
+        decode_timestamps[idx] = timestamps
+
+    async def prefill_after_warmup():
+        nonlocal prefill_ttft_ms, prefill_inject_time
+        # Wait for decode streams to stabilize
+        await asyncio.sleep(1.0)
+        prefill_inject_time = time.perf_counter()
+        prefill_payload = make_prefill_payload(model, new_prefill_tokens)
+        ts = await stream_tokens(client, url, prefill_payload)
+        if ts:
+            prefill_ttft_ms = (ts[0] - prefill_inject_time) * 1000.0
+        prefill_done_event.set()
+
+    # Launch all
+    decode_tasks = [
+        asyncio.create_task(decode_stream_with_tracking(i, p))
+        for i, p in enumerate(decode_payloads)
+    ]
+    prefill_task = asyncio.create_task(prefill_after_warmup())
+
+    await asyncio.gather(*decode_tasks, prefill_task, return_exceptions=True)
+
+    # Analyze: split decode tokens into "during prefill" and "after prefill"
+    prefill_end_time = prefill_inject_time + prefill_ttft_ms / 1000.0
+
+    tpot_during = []
+    tpot_after = []
+
+    for ts_list in decode_timestamps:
+        if len(ts_list) < WARMUP_TOKENS + 5:
+            continue
+        for i in range(WARMUP_TOKENS + 1, len(ts_list)):
+            t_prev = ts_list[i - 1]
+            t_curr = ts_list[i]
+            interval_ms = (t_curr - t_prev) * 1000.0
+
+            if prefill_inject_time <= t_prev <= prefill_end_time:
+                tpot_during.append(interval_ms)
+            elif t_curr > prefill_end_time + 0.05:  # 50ms after prefill settles
+                tpot_after.append(interval_ms)
+
+    during_arr = np.array(tpot_during) if tpot_during else np.array([0.0])
+    after_arr = np.array(tpot_after) if tpot_after else np.array([0.0])
+
+    return InterferenceResult(
+        tpot_during_prefill_p50_ms=float(np.percentile(during_arr, 50)),
+        tpot_during_prefill_p90_ms=float(np.percentile(during_arr, 90)),
+        tpot_after_prefill_p50_ms=float(np.percentile(after_arr, 50)),
+        prefill_ttft_ms=prefill_ttft_ms,
+        num_tokens_during_prefill=len(tpot_during),
+    )
+
+
+async def run_single_config(
+    client: httpx.AsyncClient,
+    url: str,
+    model: str,
+    decode_batch_size: int,
+    new_prefill_tokens: int,
+    chunk_size: int,
+    rep: int,
+    output_dir: Path,
+):
+    """Run one (D, P) configuration."""
+    config = Config(
+        decode_batch_size=decode_batch_size,
+        new_prefill_tokens=new_prefill_tokens,
+        chunk_size=chunk_size,
+        model=model,
+        repetition=rep,
+    )
+
+    print(f"  [rep {rep}] Running baseline (D={decode_batch_size})...")
+    baseline = await run_baseline(client, url, model, decode_batch_size)
+    if baseline is None:
+        print(f"  [rep {rep}] Baseline failed, skipping")
+        return
+
+    # Brief cooldown between baseline and interference
+    await asyncio.sleep(2.0)
+
+    print(f"  [rep {rep}] Running interference (D={decode_batch_size}, P={new_prefill_tokens})...")
+    interference = await run_interference(
+        client, url, model, decode_batch_size, new_prefill_tokens
+    )
+    if interference is None:
+        print(f"  [rep {rep}] Interference measurement failed, skipping")
+        return
+
+    # Compute derived metrics
+    tpot_penalty_p50 = interference.tpot_during_prefill_p50_ms - baseline.tpot_p50_ms
+    penalty_ratio = (
+        interference.tpot_during_prefill_p50_ms / baseline.tpot_p50_ms
+        if baseline.tpot_p50_ms > 0 else 0
+    )
+
+    result = {
+        "config": asdict(config),
+        "baseline": asdict(baseline),
+        "interference": asdict(interference),
+        "derived": {
+            "tpot_penalty_p50_ms": tpot_penalty_p50,
+            "tpot_penalty_ratio": penalty_ratio,
+        },
+    }
+
+    # Save
+    fname = f"D{decode_batch_size}_P{new_prefill_tokens}_rep{rep}.json"
+    out_path = output_dir / fname
+    out_path.write_text(json.dumps(result, indent=2))
+    print(f"  [rep {rep}] Done. penalty={tpot_penalty_p50:.1f}ms ratio={penalty_ratio:.2f}")
+
+
+async def main():
+    parser = argparse.ArgumentParser(description="Prefill-Decode Interference Microbenchmark")
+    parser.add_argument("--host", default="127.0.0.1")
+    parser.add_argument("--port", type=int, default=8000)
+    parser.add_argument("--model", default="Qwen3-Coder-30B-A3B-Instruct")
+    parser.add_argument("--decode-batch-sizes", default="0,1,2,4,6,8,12",
+                        help="Comma-separated decode batch sizes")
+    parser.add_argument("--prefill-tokens", default="512,1024,2048,4096,8192,16384,32768",
+                        help="Comma-separated prefill token counts")
+    parser.add_argument("--chunk-size", type=int, default=8192,
+                        help="vLLM max_num_batched_tokens (effective chunk size)")
+    parser.add_argument("--reps", type=int, default=5)
+    parser.add_argument("--output-dir", default="results/interference")
+    args = parser.parse_args()
+
+    decode_sizes = [int(x) for x in args.decode_batch_sizes.split(",")]
+    prefill_tokens = [int(x) for x in args.prefill_tokens.split(",")]
+
+    output_dir = Path(args.output_dir) / f"chunk{args.chunk_size}"
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    url = f"http://{args.host}:{args.port}/v1/chat/completions"
+    print(f"Target: {url}")
+    print(f"Model: {args.model}")
+    print(f"Chunk size: {args.chunk_size}")
+    print(f"Decode batch sizes: {decode_sizes}")
+    print(f"Prefill tokens: {prefill_tokens}")
+    print(f"Repetitions: {args.reps}")
+    print(f"Output: {output_dir}")
+    print()
+
+    async with httpx.AsyncClient(timeout=httpx.Timeout(600.0)) as client:
+        # Sanity check: is the server up?
+        try:
+            resp = await client.get(f"http://{args.host}:{args.port}/v1/models")
+            resp.raise_for_status()
+            models = resp.json()
+            print(f"Server ready. Models: {[m['id'] for m in models.get('data', [])]}")
+        except Exception as e:
+            print(f"ERROR: Cannot reach server at {args.host}:{args.port}: {e}")
+            return
+
+        total_configs = len(decode_sizes) * len(prefill_tokens)
+        done = 0
+
+        for D in decode_sizes:
+            for P in prefill_tokens:
+                done += 1
+                print(f"\n[{done}/{total_configs}] D={D}, P={P}")
+
+                for rep in range(args.reps):
+                    try:
+                        await run_single_config(
+                            client, url, args.model, D, P,
+                            args.chunk_size, rep, output_dir,
+                        )
+                    except Exception as e:
+                        print(f"  [rep {rep}] ERROR: {e}")
+
+                    # Cooldown between reps
+                    await asyncio.sleep(1.0)
+
+                # Cooldown between configs
+                await asyncio.sleep(3.0)
+
+    print("\n\nDone! Results in:", output_dir)
+    # Generate summary CSV
+    await generate_summary(output_dir, args.chunk_size)
+
+
+async def generate_summary(output_dir: Path, chunk_size: int):
+    """Aggregate all per-run JSONs into a summary CSV."""
+    import csv
+
+    rows = []
+    for f in sorted(output_dir.glob("D*_P*_rep*.json")):
+        data = json.loads(f.read_text())
+        cfg = data["config"]
+        bl = data["baseline"]
+        itf = data["interference"]
+        drv = data["derived"]
+        rows.append({
+            "chunk_size": cfg["chunk_size"],
+            "decode_batch_size": cfg["decode_batch_size"],
+            "new_prefill_tokens": cfg["new_prefill_tokens"],
+            "repetition": cfg["repetition"],
+            "tpot_baseline_p50_ms": bl["tpot_p50_ms"],
+            "tpot_baseline_p90_ms": bl["tpot_p90_ms"],
+            "tpot_during_prefill_p50_ms": itf["tpot_during_prefill_p50_ms"],
+            "tpot_during_prefill_p90_ms": itf["tpot_during_prefill_p90_ms"],
+            "tpot_after_prefill_p50_ms": itf["tpot_after_prefill_p50_ms"],
+            "prefill_ttft_ms": itf["prefill_ttft_ms"],
+            "num_tokens_during_prefill": itf["num_tokens_during_prefill"],
+            "tpot_penalty_p50_ms": drv["tpot_penalty_p50_ms"],
+            "tpot_penalty_ratio": drv["tpot_penalty_ratio"],
+        })
+
+    if not rows:
+        return
+
+    csv_path = output_dir / "summary.csv"
+    with open(csv_path, "w", newline="") as f:
+        writer = csv.DictWriter(f, fieldnames=rows[0].keys())
+        writer.writeheader()
+        writer.writerows(rows)
+    print(f"Summary CSV written: {csv_path}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/microbench/interference/launch_vllm.sh b/microbench/interference/launch_vllm.sh
new file mode 100644
index 0000000..51d2f50
--- /dev/null
+++ b/microbench/interference/launch_vllm.sh
@@ -0,0 +1,57 @@
+#!/bin/bash
+# Launch a single vLLM instance on GPU 0 for interference microbenchmark.
+# Uses TP=1, enable-chunked-prefill, enable-prefix-caching.
+#
+# Usage: bash launch_microbench1.sh [chunk_size] [port]
+#   chunk_size: max_num_batched_tokens (default: 8192)
+#   port: serving port (default: 8000)
+
+set -euo pipefail
+
+CHUNK_SIZE=${1:-8192}
+PORT=${2:-8000}
+MODEL="${MODEL:-$HOME/models/Qwen/Qwen3-Coder-30B-A3B-Instruct}"
+GPU_ID=${GPU_ID:-0}
+LOG_FILE="vllm_microbench1_chunk${CHUNK_SIZE}.log"
+
+echo "=== Interference Microbench vLLM Instance ==="
+echo "Model: $MODEL"
+echo "GPU: $GPU_ID"
+echo "Port: $PORT"
+echo "Chunk size (max_num_batched_tokens): $CHUNK_SIZE"
+echo "Log: $LOG_FILE"
+echo ""
+
+# Kill any existing vLLM on this port
+pkill -f "vllm.*--port $PORT" 2>/dev/null || true
+sleep 2
+
+CUDA_VISIBLE_DEVICES=$GPU_ID python -m vllm.entrypoints.openai.api_server \
+    --model "$MODEL" \
+    --tensor-parallel-size 1 \
+    --enable-prefix-caching \
+    --dtype auto \
+    --gpu-memory-utilization 0.9 \
+    --max-model-len 200000 \
+    --max-num-batched-tokens "$CHUNK_SIZE" \
+    --port "$PORT" \
+    --trust-remote-code \
+    --disable-log-requests \
+    2>&1 | tee "$LOG_FILE" &
+
+VLLM_PID=$!
+echo "vLLM PID: $VLLM_PID"
+echo "$VLLM_PID" > .vllm_microbench1.pid
+
+# Wait for server to be ready
+echo "Waiting for server to start..."
+for i in $(seq 1 120); do
+    if curl -s "http://127.0.0.1:$PORT/v1/models" > /dev/null 2>&1; then
+        echo "Server ready after ${i}s!"
+        exit 0
+    fi
+    sleep 1
+done
+
+echo "ERROR: Server did not start within 120s"
+exit 1
diff --git a/microbench/interference/run_sweep.sh b/microbench/interference/run_sweep.sh
new file mode 100644
index 0000000..c3c23d5
--- /dev/null
+++ b/microbench/interference/run_sweep.sh
@@ -0,0 +1,36 @@
+#!/bin/bash
+# Run the interference microbenchmark sweep.
+# Assumes vLLM is already running on the specified port.
+#
+# Usage: bash run_sweep.sh [port] [chunk_size]
+
+set -euo pipefail
+
+PORT=${1:-8000}
+CHUNK_SIZE=${2:-8192}
+REPS=${REPS:-5}
+OUTPUT_DIR="results/interference"
+
+echo "=== Interference Microbench Sweep ==="
+echo "Server: http://127.0.0.1:$PORT"
+echo "Chunk size: $CHUNK_SIZE"
+echo "Reps: $REPS"
+echo "Output: $OUTPUT_DIR"
+echo ""
+
+# Quick sanity check
+curl -sf "http://127.0.0.1:$PORT/v1/models" > /dev/null || {
+    echo "ERROR: vLLM not reachable on port $PORT"
+    exit 1
+}
+
+cd "$(dirname "$0")"
+
+python driver.py \
+    --host 127.0.0.1 \
+    --port "$PORT" \
+    --chunk-size "$CHUNK_SIZE" \
+    --decode-batch-sizes "0,1,2,4,6,8,12" \
+    --prefill-tokens "512,1024,2048,4096,8192,16384,32768" \
+    --reps "$REPS" \
+    --output-dir "$OUTPUT_DIR"
diff --git a/microbench/interference_microbench_design.md b/microbench/interference_microbench_design.md
new file mode 100644
index 0000000..9c26693
--- /dev/null
+++ b/microbench/interference_microbench_design.md
@@ -0,0 +1,317 @@
+# Prefill-Decode Interference Microbenchmark
+
+## Goal
+
+Quantify the **per-chunk TPOT degradation** caused by prefill interference on ongoing decode batches, producing a lookup table:
+
+```
+f(decode_batch_size, new_prefill_tokens, chunk_size) → TPOT_penalty_ms
+```
+
+This table is the foundation for the runtime offload decision:
+
+```
+interference_cost = num_chunks × decode_batch_size × TPOT_penalty
+if interference_cost > layerwise_transfer_cost:
+    offload()
+```
+
+---
+
+## Hardware & Model
+
+| Parameter | Value |
+|-----------|-------|
+| GPU | NVIDIA H20 96GB × 1 (single instance) |
+| Model | Qwen3-Coder-30B-A3B-Instruct (MoE 128E top-8, 3B active) |
+| TP | 1 |
+| `max_model_len` | 200000 |
+| `block_size` | 16 (vLLM default) |
+| `enable_prefix_caching` | true |
+| `enable_chunked_prefill` | true |
+| `max_num_batched_tokens` | 8192 (H20 default for openai API server) |
+| `gpu_memory_utilization` | 0.9 |
+
+---
+
+## Experiment Design
+
+### Independent Variables
+
+| Variable | Values | Rationale |
+|----------|--------|-----------|
+| `decode_batch_size` (D) | 0, 1, 2, 4, 6, 8, 12 | Covers low→saturated decode concurrency |
+| `new_prefill_tokens` (P) | 512, 1024, 2048, 4096, 8192, 16384, 32768 | Range from small warm turn to full cold heavy |
+| `chunk_size` | 2048, 4096, 8192 (default), 16384 | Sweep the dominant scheduling knob |
+
+Full sweep: 7 × 7 × 4 = 196 configurations.
+
+### Dependent Variables (Measured)
+
+| Metric | Definition | How to measure |
+|--------|-----------|----------------|
+| `TPOT_baseline` | Inter-token latency with decode-only batch (no prefill) | Send D dummy decode requests, measure steady-state TPOT |
+| `TPOT_interference` | Inter-token latency while prefill chunks execute | Measure TPOT of ongoing decode requests during the window when prefill chunks are being processed |
+| `TPOT_penalty` | `TPOT_interference - TPOT_baseline` | Per-token penalty from prefill co-execution |
+| `prefill_duration` | Wall time from prefill request submission to first token | Includes queuing + chunked execution |
+| `num_chunks_actual` | Number of scheduler iterations the prefill occupied | From vLLM engine logs or step counter |
+| `step_time_baseline` | Scheduler step duration with decode-only | From engine internals or proxy measurement |
+| `step_time_mixed` | Scheduler step duration with prefill+decode | Same |
+
+### Control Variables (Fixed per experiment)
+
+| Variable | Value | Rationale |
+|----------|-------|-----------|
+| Decode output length | 256 tokens each | Long enough to span the entire prefill window |
+| Decode context length | 4096 tokens each | Realistic session history, pre-warmed via prefix cache |
+| Prefill output length | 1 token | Minimize post-prefill decode interference |
+| KV cache state | Prefill is fully cold (no cache hit) | Worst case: maximum chunks |
+| Temperature | 0 (greedy) | Deterministic, no sampling variance |
+
+---
+
+## Protocol
+
+### Phase 1: Baseline TPOT Measurement (Decode-Only)
+
+```
+1. Launch vLLM instance (TP=1, single H20 GPU)
+2. Pre-fill D decode "seed" requests:
+   - Each has 4096-token context (pre-warmed via identical prompt prefix)
+   - Set max_tokens=256, temperature=0
+3. Once all D requests are in active decode, start timer
+4. Collect per-token timestamps for each decode request over 256 tokens
+5. Compute TPOT_baseline = median(inter-token-intervals) across all D requests
+6. Record step_time_baseline from vLLM metrics endpoint (/metrics)
+```
+
+**Warm-up**: Discard first 16 tokens per request (CUDA graph warm-up, attention ramp-up).
+
+### Phase 2: Interference Measurement (Prefill Injected)
+
+```
+1. Same setup as Phase 1: D decode requests in steady-state
+2. At token ~32 of the decode stream, inject prefill request:
+   - Input: P random tokens (no prefix cache hit)
+   - max_tokens=1
+3. Continue collecting per-token timestamps for all D decode requests
+4. Measure:
+   a. TPOT of decode requests DURING prefill window
+      (from prefill injection to prefill's first token)
+   b. TPOT of decode requests AFTER prefill completes (recovery)
+   c. Total prefill_duration
+   d. num_chunks = ceil(P / chunk_size) [verify against actual]
+```
+
+### Phase 3: Repeat for All Configurations
+
+```
+For chunk_size in [2048, 4096, 8192, 16384]:
+    Configure vLLM with --max-num-batched-tokens=chunk_size
+    Restart instance (clean KV cache state)
+    
+    For D in [0, 1, 2, 4, 6, 8, 12]:
+        For P in [512, 1024, 2048, 4096, 8192, 16384, 32768]:
+            Run Phase 1 (baseline) → record TPOT_baseline[D]
+            Run Phase 2 (interference) → record TPOT_interference[D, P]
+            Compute TPOT_penalty[D, P] = TPOT_interference - TPOT_baseline
+            Wait 5s for KV eviction and state cleanup
+```
+
+**Optimization**: Phase 1 only needs to run once per (chunk_size, D) pair since it doesn't depend on P.
+
+---
+
+## Implementation
+
+### Client Architecture
+
+```
+┌──────────────────────────────────────────────────┐
+│                  Microbench Driver                │
+├──────────────────────────────────────────────────┤
+│  1. Spawn D "background decode" streams (async)  │
+│  2. Wait for steady-state (all D in decode)      │
+│  3. Inject prefill request                       │
+│  4. Collect streaming token timestamps           │
+│  5. Compute metrics                              │
+└──────────────────────────────────────────────────┘
+         │ OpenAI-compatible streaming API
+         ▼
+┌──────────────────────────────────────────────────┐
+│           vLLM Instance (single GPU)             │
+│  --enable-chunked-prefill                        │
+│  --max-num-batched-tokens={chunk_size}           │
+│  --enable-prefix-caching                         │
+└──────────────────────────────────────────────────┘
+```
+
+### Request Construction
+
+**Decode seed requests** (to create ongoing decode batch):
+```python
+{
+    "model": "Qwen3-Coder-30B-A3B-Instruct",
+    "messages": [{"role": "user", "content": FIXED_4K_PROMPT}],
+    "max_tokens": 256,
+    "temperature": 0,
+    "stream": True
+}
+```
+
+All D requests share the same 4K prompt prefix (ensures prefix cache hit → instant prefill for seeds, isolating decode-only behavior).
+
+**Interference prefill request**:
+```python
+{
+    "model": "Qwen3-Coder-30B-A3B-Instruct",
+    "messages": [{"role": "user", "content": RANDOM_P_TOKEN_PROMPT}],
+    "max_tokens": 1,
+    "temperature": 0,
+    "stream": True
+}
+```
+
+Use random content (UUID-based) to guarantee zero prefix cache hit → forces full P-token prefill.
+
+### Timestamp Collection
+
+Use SSE streaming with `time.perf_counter_ns()` on each `data: {"choices":[{"delta":...}]}` chunk:
+
+```python
+async def collect_stream(session, url, payload) -> list[int]:
+    """Returns list of nanosecond timestamps, one per token."""
+    timestamps = []
+    async with session.post(url, json=payload) as resp:
+        async for line in resp.content:
+            if line.startswith(b"data: ") and b"[DONE]" not in line:
+                timestamps.append(time.perf_counter_ns())
+    return timestamps
+```
+
+### Steady-State Detection
+
+Before injecting prefill, verify all D requests are in active decode:
+1. Wait until each stream has emitted ≥ 32 tokens
+2. Check that the last 8 inter-token intervals are within 2× of each other (no startup variance)
+
+---
+
+## Output Format
+
+### Per-Run Record (`results/{chunk_size}/D{d}_P{p}.json`)
+
+```json
+{
+    "config": {
+        "decode_batch_size": 4,
+        "new_prefill_tokens": 8192,
+        "chunk_size": 8192,
+        "model": "Qwen3-Coder-30B-A3B-Instruct",
+        "gpu": "H20"
+    },
+    "baseline": {
+        "tpot_p50_ms": 42.3,
+        "tpot_p90_ms": 45.1,
+        "tpot_p99_ms": 48.7,
+        "step_time_ms": 43.0
+    },
+    "interference": {
+        "tpot_during_prefill_p50_ms": 89.2,
+        "tpot_during_prefill_p90_ms": 95.4,
+        "tpot_after_prefill_p50_ms": 43.1,
+        "num_chunks_actual": 1,
+        "prefill_duration_ms": 91.0,
+        "prefill_ttft_ms": 91.0
+    },
+    "derived": {
+        "tpot_penalty_p50_ms": 46.9,
+        "tpot_penalty_ratio": 1.11,
+        "total_interference_ms": 46.9,
+        "decode_tokens_delayed": 4
+    }
+}
+```
+
+### Aggregated Table (`results/interference_table.csv`)
+
+```csv
+chunk_size,decode_batch_size,new_prefill_tokens,tpot_baseline_ms,tpot_interference_ms,tpot_penalty_ms,penalty_ratio,num_chunks,prefill_duration_ms
+8192,4,8192,42.3,89.2,46.9,1.11,1,91.0
+8192,4,16384,42.3,88.5,46.2,1.09,2,178.3
+8192,8,8192,78.1,156.3,78.2,1.00,1,159.0
+...
+```
+
+---
+
+## Analysis Deliverables
+
+### 1. Interference Heatmap
+
+X-axis: `new_prefill_tokens`, Y-axis: `decode_batch_size`, Color: `tpot_penalty_ratio`
+
+Expected pattern:
+- Penalty increases with decode_batch_size (more requests disrupted)
+- Penalty per-request is roughly constant for same chunk_size (step time is dominated by the larger of prefill-chunk or decode-batch)
+- Penalty increases with num_chunks (more disrupted iterations)
+
+### 2. Total Interference Cost Model
+
+```
+total_interference_cost(D, P, chunk_size) = 
+    num_chunks(P, chunk_size) × D × tpot_penalty_per_chunk(D, chunk_size)
+```
+
+If the model fits well (R² > 0.9), it becomes the offload decision function.
+
+### 3. Break-Even Analysis
+
+For each (D, P, chunk_size), compute:
+```
+break_even_transfer_time = total_interference_cost(D, P, chunk_size)
+```
+
+If layerwise pipeline transfer cost < break_even_transfer_time, offload wins.
+
+Plot: "offload wins" region in the (D, P) space for chunk_size=8192.
+
+### 4. Sensitivity to chunk_size
+
+How does `--max-num-batched-tokens` (effective chunk size) trade off:
+- Smaller chunk → more chunks → longer total prefill → more interrupted decode steps, but each step is shorter
+- Larger chunk → fewer chunks → shorter total prefill → fewer interrupted steps, but each step takes longer
+
+---
+
+## Risks & Mitigations
+
+| Risk | Impact | Mitigation |
+|------|--------|------------|
+| CUDA graph optimization masks real penalty | Underestimate interference | Run with `--enforce-eager` as ablation |
+| vLLM internal batching merges decode+prefill differently than expected | Wrong chunk count | Verify with `/metrics` endpoint (`vllm:num_prefill_tokens_iter`) |
+| Network jitter in timestamp collection | Noisy TPOT | Run on localhost (127.0.0.1), use 5 repetitions per config |
+| KV cache pressure at high D | OOM or eviction | Keep decode context at 4K, monitor `gpu_cache_usage_perc` |
+| MoE routing variance | Non-deterministic step time | Use greedy decoding, report p50 over 5 runs |
+
+---
+
+## Execution Estimate
+
+| Phase | Time |
+|-------|------|
+| Per configuration (1 baseline + 1 interference, 5 reps) | ~3 min |
+| Full sweep (196 configs × 3 min) | ~10 hours |
+| Reduced sweep (chunk_size=8192 only, 49 configs) | ~2.5 hours |
+| Analysis & plotting | 1 hour |
+
+**Recommended**: Start with reduced sweep (default chunk_size=8192), then expand to other chunk sizes if results are promising.
+
+---
+
+## Success Criteria
+
+1. **Interference is measurable**: `tpot_penalty_ratio > 1.1` for D ≥ 4 and P ≥ 4096
+2. **Model fits**: Linear or polynomial model of `total_interference_cost` achieves R² > 0.85
+3. **Break-even exists**: There exists a realistic (D, P) region where `interference_cost > 50ms` (layerwise pipeline transfer budget)
+4. **Reproducible**: Coefficient of variation < 15% across 5 repetitions per config
diff --git a/microbench/lifecycle/driver.py b/microbench/lifecycle/driver.py
new file mode 100644
index 0000000..125ddb4
--- /dev/null
+++ b/microbench/lifecycle/driver.py
@@ -0,0 +1,412 @@
+#!/usr/bin/env python3
+"""PD Transfer Lifecycle Breakdown Microbenchmark Driver.
+
+Profiles the complete request lifecycle under PD disaggregation:
+  routing → P queue → P prefill → ZMQ handshake → RDMA transfer → D startup → D decode
+
+Three independent variables:
+  - prior_context (C): tokens already cached on D from prior turns
+  - current_new_tokens (N): tokens P must prefill and transfer
+  - output_length (O): decode tokens D generates
+
+Usage:
+    python driver.py --p-host 127.0.0.1 --p-port 8000 --d-host 127.0.0.1 --d-port 8001 \
+        --prior-contexts 0,4096,16384,32768,65536,100000 \
+        --new-tokens 512,2048,4096,8192,16384,32768 \
+        --output-lengths 1,32,128,512 \
+        --reps 5 --output-dir results/lifecycle
+"""
+
+import argparse
+import asyncio
+import hashlib
+import json
+import os
+import time
+from dataclasses import dataclass, asdict, field
+from pathlib import Path
+from typing import Optional
+
+import httpx
+import numpy as np
+
+
+@dataclass
+class LifecycleConfig:
+    prior_context: int
+    current_new_tokens: int
+    output_length: int
+    total_input_length: int
+    model: str
+    repetition: int
+
+
+@dataclass
+class LifecycleBreakdown:
+    # Client-observable timestamps (ms)
+    request_sent_to_first_token_ms: float  # TTFT (includes all server-side phases)
+    first_token_to_last_token_ms: float    # Decode time
+    e2e_ms: float                          # Total
+
+    # If server-side instrumentation available (from logs):
+    server_breakdown: Optional[dict] = None
+
+
+def make_context_prompt(num_tokens: int, session_id: str = "default") -> str:
+    """Generate deterministic prompt content of approximately num_tokens tokens.
+    Uses a fixed seed so the same (num_tokens, session_id) always produces the same prefix
+    (required for prefix cache hits across calls).
+    """
+    if num_tokens == 0:
+        return ""
+    # Each "chunk" is ~50 tokens. Generate enough chunks.
+    parts = []
+    chunks_needed = (num_tokens // 50) + 1
+    for i in range(chunks_needed):
+        seed = hashlib.sha256(f"{session_id}_ctx_{i}".encode()).hexdigest()
+        parts.append(
+            f"[Context block {i}] The system processes request {seed[:16]} "
+            f"with parameters alpha={seed[16:20]} beta={seed[20:24]} "
+            f"resulting in state transition {seed[24:32]}. "
+        )
+    return " ".join(parts)
+
+
+def make_new_tokens_prompt(num_tokens: int, unique_id: str) -> str:
+    """Generate unique new content (guaranteed no prefix cache hit)."""
+    parts = []
+    chunks_needed = (num_tokens // 50) + 1
+    for i in range(chunks_needed):
+        seed = hashlib.sha256(f"{unique_id}_new_{i}_{time.time_ns()}".encode()).hexdigest()
+        parts.append(
+            f"[New block {i}] Analyze document {seed[:16]} considering "
+            f"factors {seed[16:24]} and constraints {seed[24:32]}. "
+        )
+    return " ".join(parts)
+
+
+async def seed_prefix_cache(
+    client: httpx.AsyncClient, url: str, model: str, num_tokens: int, session_id: str
+) -> bool:
+    """Send a request to D to warm its prefix cache with num_tokens of context.
+    Returns True if successful.
+    """
+    if num_tokens == 0:
+        return True
+
+    prompt = make_context_prompt(num_tokens, session_id)
+    payload = {
+        "model": model,
+        "messages": [{"role": "user", "content": prompt}],
+        "max_tokens": 1,
+        "temperature": 0,
+        "stream": False,
+    }
+
+    try:
+        resp = await client.post(url, json=payload, timeout=120.0)
+        resp.raise_for_status()
+        result = resp.json()
+        usage = result.get("usage", {})
+        prompt_tokens = usage.get("prompt_tokens", 0)
+        print(f"    Cache seeded: {prompt_tokens} prompt tokens processed")
+        return True
+    except Exception as e:
+        print(f"    Cache seed FAILED: {e}")
+        return False
+
+
+async def measure_lifecycle(
+    client: httpx.AsyncClient,
+    url: str,
+    model: str,
+    prior_context: int,
+    current_new_tokens: int,
+    output_length: int,
+    session_id: str,
+) -> Optional[LifecycleBreakdown]:
+    """Send a PD-sep request and measure lifecycle timestamps.
+
+    The request has:
+      - prefix: prior_context tokens (should hit D's prefix cache)
+      - suffix: current_new_tokens tokens (must be prefilled by P and transferred)
+    """
+    # Build prompt: shared prefix (cached on D) + unique suffix (cold)
+    prefix = make_context_prompt(prior_context, session_id)
+    suffix = make_new_tokens_prompt(current_new_tokens, f"{session_id}_{time.time_ns()}")
+    full_prompt = prefix + "\n\n" + suffix if prefix else suffix
+
+    payload = {
+        "model": model,
+        "messages": [{"role": "user", "content": full_prompt}],
+        "max_tokens": output_length,
+        "temperature": 0,
+        "stream": True,
+    }
+
+    timestamps = []
+    t_send = time.perf_counter()
+
+    try:
+        async with client.stream("POST", url, json=payload, timeout=300.0) as resp:
+            resp.raise_for_status()
+            async for line in resp.aiter_lines():
+                if line.startswith("data: "):
+                    data = line[6:]
+                    if data.strip() == "[DONE]":
+                        break
+                    try:
+                        chunk = json.loads(data)
+                        choices = chunk.get("choices", [])
+                        if not choices:
+                            continue
+                        delta = choices[0].get("delta", {})
+                        if "role" in delta:
+                            continue
+                        timestamps.append(time.perf_counter())
+                    except json.JSONDecodeError:
+                        continue
+    except Exception as e:
+        print(f"    Request failed: {e}")
+        return None
+
+    if not timestamps:
+        print("    No tokens received")
+        return None
+
+    t_first = timestamps[0]
+    t_last = timestamps[-1] if len(timestamps) > 1 else t_first
+
+    ttft_ms = (t_first - t_send) * 1000.0
+    decode_ms = (t_last - t_first) * 1000.0
+    e2e_ms = (t_last - t_send) * 1000.0
+
+    return LifecycleBreakdown(
+        request_sent_to_first_token_ms=ttft_ms,
+        first_token_to_last_token_ms=decode_ms,
+        e2e_ms=e2e_ms,
+    )
+
+
+async def measure_colocated_baseline(
+    client: httpx.AsyncClient,
+    url: str,
+    model: str,
+    prior_context: int,
+    current_new_tokens: int,
+    output_length: int,
+    session_id: str,
+) -> Optional[LifecycleBreakdown]:
+    """Same request on combined (no PD-sep) instance for comparison."""
+    return await measure_lifecycle(
+        client, url, model, prior_context, current_new_tokens, output_length, session_id
+    )
+
+
+async def run_config(
+    client: httpx.AsyncClient,
+    pdsep_url: str,
+    colo_url: Optional[str],
+    model: str,
+    prior_context: int,
+    current_new_tokens: int,
+    output_length: int,
+    rep: int,
+    output_dir: Path,
+    session_id: str,
+) -> dict:
+    """Run one configuration: PD-sep measurement + optional colo baseline."""
+
+    config = LifecycleConfig(
+        prior_context=prior_context,
+        current_new_tokens=current_new_tokens,
+        output_length=output_length,
+        total_input_length=prior_context + current_new_tokens,
+        model=model,
+        repetition=rep,
+    )
+
+    # PD-sep measurement
+    pdsep_result = await measure_lifecycle(
+        client, pdsep_url, model, prior_context, current_new_tokens, output_length, session_id
+    )
+
+    # Colocated baseline (if URL provided)
+    colo_result = None
+    if colo_url:
+        colo_result = await measure_colocated_baseline(
+            client, colo_url, model, prior_context, current_new_tokens, output_length, session_id
+        )
+
+    result = {
+        "config": asdict(config),
+        "pdsep": asdict(pdsep_result) if pdsep_result else None,
+        "colocated": asdict(colo_result) if colo_result else None,
+    }
+
+    if pdsep_result and colo_result:
+        result["overhead"] = {
+            "ttft_overhead_ms": pdsep_result.request_sent_to_first_token_ms - colo_result.request_sent_to_first_token_ms,
+            "e2e_overhead_ms": pdsep_result.e2e_ms - colo_result.e2e_ms,
+        }
+
+    # Save
+    fname = f"C{prior_context}_N{current_new_tokens}_O{output_length}_rep{rep}.json"
+    out_path = output_dir / fname
+    out_path.write_text(json.dumps(result, indent=2))
+
+    if pdsep_result:
+        print(f"    TTFT={pdsep_result.request_sent_to_first_token_ms:.0f}ms "
+              f"decode={pdsep_result.first_token_to_last_token_ms:.0f}ms "
+              f"E2E={pdsep_result.e2e_ms:.0f}ms")
+
+    return result
+
+
+async def main():
+    parser = argparse.ArgumentParser(description="PD Transfer Lifecycle Breakdown Microbench")
+    parser.add_argument("--pdsep-url", required=True,
+                        help="PD-sep endpoint URL (proxy or D instance)")
+    parser.add_argument("--colo-url", default=None,
+                        help="Co-located baseline URL (optional, for overhead comparison)")
+    parser.add_argument("--seed-url", default=None,
+                        help="D-instance URL for cache seeding (if different from pdsep-url)")
+    parser.add_argument("--model", default="Qwen3-Coder-30B-A3B-Instruct")
+    parser.add_argument("--prior-contexts", default="0,4096,16384,32768,65536,100000",
+                        help="Comma-separated prior context sizes")
+    parser.add_argument("--new-tokens", default="512,2048,4096,8192,16384,32768",
+                        help="Comma-separated new token counts")
+    parser.add_argument("--output-lengths", default="1,32,128,512",
+                        help="Comma-separated output lengths")
+    parser.add_argument("--reps", type=int, default=5)
+    parser.add_argument("--output-dir", default="results/lifecycle")
+    parser.add_argument("--session-id", default="bench_session_0",
+                        help="Session ID for deterministic prefix generation")
+    args = parser.parse_args()
+
+    prior_contexts = [int(x) for x in args.prior_contexts.split(",")]
+    new_tokens_list = [int(x) for x in args.new_tokens.split(",")]
+    output_lengths = [int(x) for x in args.output_lengths.split(",")]
+
+    output_dir = Path(args.output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    pdsep_url = args.pdsep_url.rstrip("/") + "/v1/chat/completions"
+    colo_url = (args.colo_url.rstrip("/") + "/v1/chat/completions") if args.colo_url else None
+    seed_url = args.seed_url or args.pdsep_url
+    seed_endpoint = seed_url.rstrip("/") + "/v1/chat/completions"
+
+    print(f"PD-sep endpoint: {pdsep_url}")
+    print(f"Colo endpoint: {colo_url or 'none'}")
+    print(f"Seed endpoint: {seed_endpoint}")
+    print(f"Model: {args.model}")
+    print(f"Prior contexts: {prior_contexts}")
+    print(f"New tokens: {new_tokens_list}")
+    print(f"Output lengths: {output_lengths}")
+    print(f"Repetitions: {args.reps}")
+    print()
+
+    total_configs = len(prior_contexts) * len(new_tokens_list) * len(output_lengths)
+    done = 0
+
+    async with httpx.AsyncClient(timeout=httpx.Timeout(600.0)) as client:
+        # Quick connectivity check (proxy may not implement /v1/models)
+        print("Starting sweep (server connectivity will be verified on first request)...")
+
+        for C in prior_contexts:
+            print(f"\n{'='*60}")
+            print(f"Prior context C={C} tokens")
+            print(f"{'='*60}")
+
+            # Seed D's prefix cache
+            if C > 0:
+                print(f"  Seeding D prefix cache with {C} tokens...")
+                success = await seed_prefix_cache(
+                    client, seed_endpoint, args.model, C, args.session_id
+                )
+                if not success:
+                    print(f"  SKIP all configs with C={C} (cache seed failed)")
+                    done += len(new_tokens_list) * len(output_lengths)
+                    continue
+                await asyncio.sleep(2.0)
+
+            for N in new_tokens_list:
+                for O in output_lengths:
+                    done += 1
+                    print(f"\n  [{done}/{total_configs}] C={C}, N={N}, O={O}")
+
+                    for rep in range(args.reps):
+                        try:
+                            await run_config(
+                                client, pdsep_url, colo_url, args.model,
+                                C, N, O, rep, output_dir, args.session_id,
+                            )
+                        except Exception as e:
+                            print(f"    [rep {rep}] ERROR: {e}")
+                        await asyncio.sleep(1.0)
+
+                    await asyncio.sleep(2.0)
+
+            # Note: we do NOT evict cache between C values because each C uses
+            # a deterministic prefix. Larger C is a superset of smaller C.
+
+    print(f"\n\nDone! Results in: {output_dir}")
+    generate_summary_csv(output_dir)
+
+
+def generate_summary_csv(output_dir: Path):
+    """Aggregate results into summary CSV."""
+    import csv
+
+    rows = []
+    for f in sorted(output_dir.glob("C*_N*_O*_rep*.json")):
+        data = json.loads(f.read_text())
+        cfg = data["config"]
+        pdsep = data.get("pdsep")
+        colo = data.get("colocated")
+        overhead = data.get("overhead")
+
+        row = {
+            "prior_context": cfg["prior_context"],
+            "new_tokens": cfg["current_new_tokens"],
+            "output_length": cfg["output_length"],
+            "total_input": cfg["total_input_length"],
+            "repetition": cfg["repetition"],
+        }
+
+        if pdsep:
+            row["pdsep_ttft_ms"] = pdsep["request_sent_to_first_token_ms"]
+            row["pdsep_decode_ms"] = pdsep["first_token_to_last_token_ms"]
+            row["pdsep_e2e_ms"] = pdsep["e2e_ms"]
+
+        if colo:
+            row["colo_ttft_ms"] = colo["request_sent_to_first_token_ms"]
+            row["colo_decode_ms"] = colo["first_token_to_last_token_ms"]
+            row["colo_e2e_ms"] = colo["e2e_ms"]
+
+        if overhead:
+            row["ttft_overhead_ms"] = overhead["ttft_overhead_ms"]
+            row["e2e_overhead_ms"] = overhead["e2e_overhead_ms"]
+
+        rows.append(row)
+
+    if not rows:
+        return
+
+    csv_path = output_dir / "summary.csv"
+    fieldnames = list(rows[0].keys())
+    # Ensure all rows have all fields
+    all_fields = set()
+    for r in rows:
+        all_fields.update(r.keys())
+    fieldnames = sorted(all_fields)
+
+    with open(csv_path, "w", newline="") as f:
+        writer = csv.DictWriter(f, fieldnames=fieldnames, extrasaction="ignore")
+        writer.writeheader()
+        writer.writerows(rows)
+    print(f"Summary CSV written: {csv_path}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/microbench/lifecycle/launch_pd_pair.sh b/microbench/lifecycle/launch_pd_pair.sh
new file mode 100644
index 0000000..26484d9
--- /dev/null
+++ b/microbench/lifecycle/launch_pd_pair.sh
@@ -0,0 +1,112 @@
+#!/bin/bash
+# Launch PD-separated pair (TP=1 each) for lifecycle microbenchmark.
+# Uses GPUs 1 (prefill) and 2 (decode) to avoid conflicting with Microbench 1 on GPU 0.
+#
+# Usage: bash launch_pd_pair.sh
+#   Requires: ~/agentic-kv/.venv with vLLM 0.18.1 + Mooncake
+
+set -euo pipefail
+
+VENV="$HOME/agentic-kv/.venv/bin"
+PYTHON="$VENV/python"
+MODEL_PATH="${MODEL_PATH:-$HOME/models/Qwen/Qwen3-Coder-30B-A3B-Instruct}"
+
+PREFILL_PORT=8010
+DECODE_PORT=8020
+BOOTSTRAP_PORT=8998
+
+PREFILL_GPU=1
+DECODE_GPU=2
+
+LOG_DIR="$HOME/agentic-kv/microbench/lifecycle/logs"
+mkdir -p "$LOG_DIR"
+
+trap 'echo "Cleaning up..."; kill $(jobs -p) 2>/dev/null; wait 2>/dev/null' EXIT INT TERM
+
+echo "=== PD Lifecycle Microbench: PD-separated pair ==="
+echo "  Model:     $MODEL_PATH"
+echo "  Prefill:   GPU $PREFILL_GPU, port $PREFILL_PORT, bootstrap $BOOTSTRAP_PORT"
+echo "  Decode:    GPU $DECODE_GPU, port $DECODE_PORT"
+echo ""
+
+# Start prefill instance (KV producer)
+echo "[1/2] Starting prefill instance on GPU $PREFILL_GPU..."
+VLLM_MOONCAKE_BOOTSTRAP_PORT=$BOOTSTRAP_PORT \
+CUDA_VISIBLE_DEVICES=$PREFILL_GPU \
+$PYTHON -m vllm.entrypoints.openai.api_server \
+    --model "$MODEL_PATH" \
+    --host 0.0.0.0 \
+    --port $PREFILL_PORT \
+    --tensor-parallel-size 1 \
+    --trust-remote-code \
+    --enable-prefix-caching \
+    --dtype auto \
+    --gpu-memory-utilization 0.9 \
+    --max-model-len 200000 \
+    --no-enable-log-requests \
+    --kv-transfer-config \
+    '{"kv_connector":"MooncakeConnector","kv_role":"kv_producer"}' \
+    2>&1 | tee "$LOG_DIR/prefill.log" &
+PREFILL_PID=$!
+echo "  Prefill PID=$PREFILL_PID"
+
+# Wait for prefill to be ready
+echo "  Waiting for prefill instance..."
+for i in $(seq 1 180); do
+    if curl -s "http://127.0.0.1:$PREFILL_PORT/v1/models" > /dev/null 2>&1; then
+        echo "  Prefill ready after ${i}s"
+        break
+    fi
+    if [ $i -eq 180 ]; then
+        echo "  ERROR: Prefill did not start within 180s"
+        exit 1
+    fi
+    sleep 1
+done
+
+# Start decode instance (KV consumer)
+echo "[2/2] Starting decode instance on GPU $DECODE_GPU..."
+CUDA_VISIBLE_DEVICES=$DECODE_GPU \
+$PYTHON -m vllm.entrypoints.openai.api_server \
+    --model "$MODEL_PATH" \
+    --host 0.0.0.0 \
+    --port $DECODE_PORT \
+    --tensor-parallel-size 1 \
+    --trust-remote-code \
+    --enable-prefix-caching \
+    --dtype auto \
+    --gpu-memory-utilization 0.9 \
+    --max-model-len 200000 \
+    --no-enable-log-requests \
+    --kv-transfer-config \
+    "{\"kv_connector\":\"MooncakeConnector\",\"kv_role\":\"kv_consumer\",\"kv_connector_extra_config\":{\"prefill_addr\":\"127.0.0.1:$BOOTSTRAP_PORT\"}}" \
+    2>&1 | tee "$LOG_DIR/decode.log" &
+DECODE_PID=$!
+echo "  Decode PID=$DECODE_PID"
+
+# Wait for decode to be ready
+echo "  Waiting for decode instance..."
+for i in $(seq 1 180); do
+    if curl -s "http://127.0.0.1:$DECODE_PORT/v1/models" > /dev/null 2>&1; then
+        echo "  Decode ready after ${i}s"
+        break
+    fi
+    if [ $i -eq 180 ]; then
+        echo "  ERROR: Decode did not start within 180s"
+        exit 1
+    fi
+    sleep 1
+done
+
+echo ""
+echo "=== Both instances ready ==="
+echo "  Prefill: http://127.0.0.1:$PREFILL_PORT (PID $PREFILL_PID)"
+echo "  Decode:  http://127.0.0.1:$DECODE_PORT (PID $DECODE_PID)"
+echo ""
+echo "  Prefill PID: $PREFILL_PID" > "$LOG_DIR/.pids"
+echo "  Decode PID: $DECODE_PID" >> "$LOG_DIR/.pids"
+echo "$PREFILL_PID" > "$LOG_DIR/.prefill.pid"
+echo "$DECODE_PID" > "$LOG_DIR/.decode.pid"
+
+echo "Press Ctrl+C to stop both instances."
+wait
diff --git a/microbench/transfer_lifecycle_design.md b/microbench/transfer_lifecycle_design.md
new file mode 100644
index 0000000..648c0d6
--- /dev/null
+++ b/microbench/transfer_lifecycle_design.md
@@ -0,0 +1,364 @@
+# PD Transfer Lifecycle Breakdown Microbenchmark
+
+## Goal
+
+Profile the **complete request lifecycle** under PD disaggregation, with emphasis on the P→D KV transfer stage. Produce a per-phase latency breakdown as a function of three independent variables:
+
+```
+breakdown(prior_context, current_new_tokens, output_length) → {
+    routing_ms, p_queue_ms, p_prefill_ms, 
+    zmq_handshake_ms, rdma_transfer_ms, transfer_completion_signal_ms,
+    d_block_alloc_ms, d_cache_promotion_ms, d_schedule_ms, d_first_decode_ms,
+    d_decode_total_ms
+}
+```
+
+---
+
+## Background: vLLM PD Transfer Semantics
+
+Transfer is **incremental**:
+- D uses its local prefix cache for prior turns (blocks with matching hashes)
+- P only transfers the **delta**: `ext_tokens = remote_total - D_local_cache_hits`
+- D combines: local prefix cache + remote-transferred blocks + locally-computed remainder
+
+Therefore, `prior_context` (already cached on D) determines how much P actually transfers.
+
+---
+
+## Hardware & Model
+
+| Parameter | Value |
+|-----------|-------|
+| GPUs | 2× H20 96GB (1 for P, 1 for D), NVLink/RDMA connected |
+| Model | Qwen3-Coder-30B-A3B-Instruct |
+| TP | 1 per instance |
+| Transfer | Mooncake (`kv_producer` / `kv_consumer`) |
+| `enable_prefix_caching` | true |
+| `enable_chunked_prefill` | true |
+| `max_num_batched_tokens` | 8192 |
+| `gpu_memory_utilization` | 0.9 |
+
+---
+
+## Independent Variables
+
+| Variable | Symbol | Values | Meaning |
+|----------|--------|--------|---------|
+| Prior context (D-side cached) | `C` | 0, 4k, 16k, 32k, 64k, 100k | Tokens from prior turns, already in D's prefix cache |
+| Current new tokens | `N` | 512, 2k, 4k, 8k, 16k, 32k | Tokens P must prefill and transfer (the delta) |
+| Output length | `O` | 1, 32, 128, 512 | Decode tokens D generates after receiving KV |
+
+Sweep: 6 × 6 × 4 = 144 configurations.
+
+**Total input_length per request** = `C + N`.
+
+---
+
+## Lifecycle Phases & Instrumentation
+
+### Phase Diagram
+
+```
+Time ─────────────────────────────────────────────────────────────────────►
+
+[Routing] [P Queue] [P Prefill (chunked)] [Transfer] [D Startup] [D Decode]
+   t0       t1          t2          t3       t4  t5     t6  t7      t8   t9
+
+t0: Request arrives at proxy/router
+t1: Request dispatched to P instance (P receives HTTP request)
+t2: P scheduler picks up request (first prefill chunk starts)
+t3: P prefill completes (last chunk done, all KV blocks ready)
+t4: P sends ZMQ metadata to D (or D sends block alloc to P)
+t5: First RDMA write issued
+t6: Last RDMA write completes (all blocks landed on D GPU)
+t7: D receives completion signal (ZMQ response parsed)
+t8: D scheduler promotes request from WAITING_FOR_REMOTE_KVS → schedulable
+t9: D first decode token emitted
+t10: D final output token emitted
+```
+
+### Instrumentation Points
+
+| Timestamp | Where to instrument | Method |
+|-----------|-------------------|--------|
+| `t0` | Proxy `pick_instance()` entry | Proxy log with `time.perf_counter_ns()` |
+| `t1` | Proxy forwards to P (HTTP send complete) | Proxy log |
+| `t2` | P scheduler `schedule()` — request leaves WAITING | vLLM patch: log in scheduler |
+| `t3` | P `request_finished()` or `save_kv_layer` last layer | vLLM patch: log in connector `record_send_reqs` |
+| `t4` | P `send_kv_to_decode`: ZMQ metadata received by handler | Connector log: before `_build_transfer_params` |
+| `t5` | P `batch_transfer_sync_write` entry | Connector log |
+| `t6` | P `batch_transfer_sync_write` return | Connector log (ret_value == 0) |
+| `t7` | D `process_pulling_result`: `finished_recving_reqs.add()` | Connector log |
+| `t8` | D scheduler `_try_promote_blocked_waiting_request` success | Scheduler log |
+| `t9` | D first token streamed to client | Client-side SSE timestamp |
+| `t10` | D last token streamed to client | Client-side SSE timestamp |
+
+### Derived Metrics
+
+| Metric | Formula | What it tells us |
+|--------|---------|-----------------|
+| `routing_latency` | t1 - t0 | Proxy overhead |
+| `p_queue_time` | t2 - t1 | P scheduling delay |
+| `p_prefill_time` | t3 - t2 | Actual prefill compute (chunked) |
+| `zmq_handshake` | t5 - t3 | ZMQ coordination overhead (P ready → RDMA starts) |
+| `rdma_transfer_time` | t6 - t5 | Pure RDMA data movement |
+| `transfer_signal_latency` | t7 - t6 | Completion detection (ZMQ response + asyncio poll) |
+| `d_promotion_latency` | t8 - t7 | Scheduler step delay until promotion |
+| `d_first_token_latency` | t9 - t8 | D compute startup (1 token forward + sampling) |
+| `d_decode_time` | t10 - t9 | Decode generation (O-1 tokens) |
+| **`transfer_total`** | t7 - t3 | **End-to-end transfer overhead** (the key number) |
+| **`ttft_overhead_vs_colo`** | t9 - t0 - p_prefill_time | Extra latency vs if the same request ran on combined instance |
+
+---
+
+## Transfer Internal Breakdown
+
+For the `rdma_transfer_time` phase, instrument further:
+
+| Sub-phase | How to measure |
+|-----------|---------------|
+| `build_transfer_params` | Time `_build_transfer_params()` call |
+| `rdma_write_submit` | Time from `batch_transfer_sync_write` entry to first RDMA CQ completion (if available) |
+| `rdma_write_total` | Full `batch_transfer_sync_write` duration |
+| `bytes_transferred` | `sum(lengths)` from transfer params |
+| `num_rdma_ops` | `len(src_ptrs)` (number of RDMA write operations) |
+| `effective_bandwidth` | `bytes_transferred / rdma_write_total` |
+| `num_layers_transferred` | Count of unique layers in transfer |
+| `num_blocks_transferred` | Count of blocks |
+
+Expected relationships:
+- `bytes_transferred = num_blocks × block_size_bytes × num_layers`
+- `block_size_bytes = 16 tokens × 2(KV) × num_kv_heads × head_dim × dtype_size`
+- `rdma_transfer_time ≈ bytes_transferred / RDMA_bandwidth + per_op_latency × num_ops`
+
+---
+
+## Protocol
+
+### Setup: Warm D's Prefix Cache
+
+To control `prior_context` (C), we need D to have prior-turn KV in its local prefix cache:
+
+```
+Phase 0: Seed D's cache
+  1. For each config with C > 0:
+     - Send a request with C-token prompt directly to D (combined mode, no PD-sep)
+     - Let it generate 1 token → D now has C tokens in prefix cache
+     - Verify via /metrics that prefix cache utilization increased
+  2. Switch D to kv_consumer mode (or keep combined + use kv_transfer_params override)
+```
+
+Alternative: Use D in `kv_both` mode (combined + Mooncake enabled), then send PD-sep requests with `kv_transfer_params` that explicitly request remote prefill.
+
+### Main Experiment
+
+```
+For C in [0, 4k, 16k, 32k, 64k, 100k]:
+    Seed D's prefix cache with C tokens (Phase 0)
+    
+    For N in [512, 2k, 4k, 8k, 16k, 32k]:
+        For O in [1, 32, 128, 512]:
+            Construct request:
+                input = C_token_prefix + N_random_new_tokens  (total = C+N)
+                max_tokens = O
+                kv_transfer_params = {do_remote_prefill: true, ...}
+            
+            Send request through proxy → P → D
+            Collect all timestamps (t0..t10)
+            Repeat 5 times
+            
+            Record breakdown
+    
+    Evict D's cache (restart or send cache-clearing requests)
+```
+
+### D-Side Cache Verification
+
+Before each measurement, verify D's cache state:
+```python
+# Check that D has exactly C tokens cached
+resp = httpx.get(f"http://{d_host}:{d_port}/metrics")
+# Parse vllm:prefix_cache_hit_rate or gpu_prefix_cache_hit_rate
+# Or use internal API to query cached block count
+```
+
+---
+
+## vLLM Instrumentation Patch
+
+Minimal patch to `mooncake_connector.py` for timestamp collection:
+
+```python
+# Add at top
+import time
+_PROFILE_LOG = []  # or write to file
+
+# In send_kv_to_decode(), around line 800-990:
+async def send_kv_to_decode(self, ...):
+    t_ready = time.perf_counter_ns()  # P prefill done, ready to send
+    
+    # ... ZMQ receive metadata from D ...
+    t_zmq_recv = time.perf_counter_ns()
+    
+    # ... build transfer params ...
+    t_params_built = time.perf_counter_ns()
+    
+    ret_value = self.engine.batch_transfer_sync_write(...)
+    t_rdma_done = time.perf_counter_ns()
+    
+    # ... send ZMQ response ...
+    t_zmq_sent = time.perf_counter_ns()
+    
+    _PROFILE_LOG.append({
+        "req_id": req_id,
+        "bytes": sum(lengths),
+        "num_ops": len(src_ptrs),
+        "t_ready": t_ready,
+        "t_zmq_recv": t_zmq_recv,
+        "t_params_built": t_params_built, 
+        "t_rdma_done": t_rdma_done,
+        "t_zmq_sent": t_zmq_sent,
+    })
+```
+
+Similar patches needed in:
+- `scheduler.py`: Log `t_schedule_start`, `t_promote`
+- `process_pulling_result()`: Log `t_recv_complete`
+
+---
+
+## Output Format
+
+### Per-Request Record (`results/lifecycle/C{c}_N{n}_O{o}_rep{r}.json`)
+
+```json
+{
+    "config": {
+        "prior_context": 32000,
+        "current_new_tokens": 8192,
+        "output_length": 128,
+        "total_input_length": 40192
+    },
+    "timestamps_ns": {
+        "t0_proxy_recv": 1000000000,
+        "t1_proxy_dispatch": 1000050000,
+        "t2_p_schedule": 1000200000,
+        "t3_p_prefill_done": 1001100000,
+        "t4_zmq_metadata": 1001150000,
+        "t5_rdma_start": 1001200000,
+        "t6_rdma_complete": 1002300000,
+        "t7_d_recv_signal": 1002350000,
+        "t8_d_promoted": 1002500000,
+        "t9_d_first_token": 1002600000,
+        "t10_d_last_token": 1003800000
+    },
+    "breakdown_ms": {
+        "routing": 0.05,
+        "p_queue": 0.15,
+        "p_prefill": 0.90,
+        "zmq_handshake": 0.05,
+        "rdma_transfer": 1.10,
+        "transfer_signal": 0.05,
+        "d_promotion": 0.15,
+        "d_first_token": 0.10,
+        "d_decode": 1.20,
+        "transfer_total": 1.20,
+        "e2e": 3.80
+    },
+    "transfer_detail": {
+        "bytes_transferred": 268435456,
+        "num_rdma_ops": 512,
+        "num_blocks": 512,
+        "num_layers": 32,
+        "build_params_ms": 0.8,
+        "rdma_write_ms": 1100.0,
+        "effective_bw_gbps": 195.2
+    }
+}
+```
+
+### Aggregated Summary (`results/lifecycle/summary.csv`)
+
+```csv
+prior_context,new_tokens,output_length,p_prefill_ms,rdma_transfer_ms,transfer_total_ms,d_decode_ms,e2e_ms,bytes_GB,bw_gbps,ttft_overhead_ms
+0,8192,128,890,1100,1200,480,2620,0.268,195,1200
+32000,8192,128,890,1100,1200,480,2620,0.268,195,1200
+64000,8192,128,890,1100,1200,480,2620,0.268,195,1200
+0,32768,128,3200,4400,4500,480,8230,1.073,195,4500
+```
+
+---
+
+## Analysis Deliverables
+
+### 1. Stacked Bar Chart: Lifecycle Breakdown vs N (new tokens)
+
+X-axis: `current_new_tokens`
+Y-axis: Time (ms)
+Stacked bars: routing | p_queue | p_prefill | zmq | rdma_transfer | signal | d_promotion | d_decode
+
+Separate subplot rows for each `prior_context` value.
+
+### 2. Transfer Bandwidth Characterization
+
+Plot `effective_bandwidth` vs `bytes_transferred`:
+- Expected: bandwidth increases with transfer size (amortizes per-op latency)
+- Identify the "bandwidth knee" — minimum transfer size for near-peak bandwidth
+- Compare against theoretical 200 Gbps RDMA limit
+
+### 3. Transfer Cost Model
+
+Fit: `rdma_transfer_ms = α + β × bytes_transferred`
+- α = per-operation fixed cost (ZMQ + scheduling)
+- β = 1/bandwidth (bytes → time)
+
+### 4. Overhead vs Co-Located Baseline
+
+For each config, also measure the same request on a **combined** (no PD-sep) instance:
+- `colo_ttft` = time from request to first token on combined instance
+- `pdsep_overhead = pdsep_ttft - colo_ttft`
+
+Plot: overhead as function of (C, N) — when does PD-sep become net-negative?
+
+### 5. Impact of Prior Context on Transfer Volume
+
+Since transfer is incremental:
+- When `C` increases (D has more cached), actual `bytes_transferred` should stay constant (≈ N × per_token_kv_size)
+- Verify this — if NOT constant, there's a bug in incremental transfer logic
+- Plot actual `bytes_transferred` vs C for fixed N
+
+---
+
+## Risks & Mitigations
+
+| Risk | Impact | Mitigation |
+|------|--------|------------|
+| Clock skew between P and D processes | Wrong cross-instance durations | Use single-machine 2-GPU setup, share `time.perf_counter_ns()` clock |
+| P and D scheduler step async | Promotion delayed by step interval | Record D's scheduler step frequency, subtract 0.5×step from d_promotion |
+| Prefix cache eviction during experiment | C not actually cached | Monitor cache metrics, use small enough working set |
+| Mooncake connection pool warmup | First transfer slower | Discard first 2 repetitions, use reps 3-5 |
+| vLLM internal queuing at high C+N | OOM or scheduling delays | Monitor `gpu_cache_usage_perc`, keep C+N ≤ 132k |
+
+---
+
+## Execution Estimate
+
+| Phase | Time |
+|-------|------|
+| vLLM patch development & validation | 2 hours |
+| Per configuration (5 reps × ~10s each) | ~50s |
+| Full sweep (144 configs × 50s) | ~2 hours |
+| Cache seeding overhead (6 prior_context levels) | ~30 min |
+| Analysis & plotting | 2 hours |
+| **Total** | **~7 hours** |
+
+---
+
+## Success Criteria
+
+1. **Breakdown is complete**: All phases sum to E2E (residual < 5%)
+2. **Transfer dominates**: `rdma_transfer_ms > p_prefill_ms` for N ≥ 4k (confirms current bottleneck hypothesis)
+3. **Bandwidth model fits**: Linear model R² > 0.95
+4. **Incremental verified**: `bytes_transferred` independent of `prior_context` for fixed N
+5. **Overhead quantified**: Clear threshold (N, C) where PD-sep overhead exceeds co-located execution