# agentic-kv

Serving agentic LLM workloads by keeping the KV working set in GPU HBM
(GPU-hit-first). Research outline: [`PAPER_OUTLINE.md`](PAPER_OUTLINE.md).
Evidence + experiments: [`v2/`](v2/).

---

## ⚠️ Benchmarking methodology — read this first

> **Replay agentic traces with `--dispatch-mode thinktime`, not the default
> `tracets`.** It is the faithful, more realistic load — and the dispatch mode
> materially changes the performance you measure.

The replayer offers two ways to time each turn:

| mode | turn-k dispatched at | what it models |
|---|---|---|
| `tracets` (default) | `max(prev_turn_finished, trace_ts)` | absolute production schedule |
| **`thinktime` (use this)** | `prev_turn_finished + time_to_parent_chat` | real closed-loop agent pacing |

**Why it matters.** `tracets` collapses the inter-turn think-time to ~0 whenever
the system falls behind (it fires the next turn immediately because the trace
timestamp is already in the past). That manufactures **artificial request
bursts** — spiking instantaneous concurrency → KV-pool pressure → preemption →
inflated tail latency and wasted throughput. `thinktime` keeps each turn's real
gap (tool-exec + agent think), so the offered load is what a real agent produces.

**Measured (w600 first-300s window, 8×H20, round-robin, 100% completion):**

| metric (N=8) | `tracets` (Mode 1) | **`thinktime` (Mode 2)** | Δ |
|---|---:|---:|---:|
| E2E p90 | 102.8 s | **73.5 s** | **−28%** |
| E2E p99 | 245 s | **227 s** | −7% |
| TTFT p90 | 56.1 s | **39.7 s** | **−29%** |
| system TPS | 111.8 | **119.3** | **+7%** |
| wall-clock | 967 s | **787 s** | −19% |
| TPOT p90 | 0.174 s | 0.188 s | ~flat |

So under realistic capacity, `tracets` makes the system look **~30% worse on
tail latency** than it actually is. Tell-tale: scaling 6→8 instances barely helped
`tracets` (975→967 s — its bursts re-saturate regardless of capacity) but helped
`thinktime` a lot (1125→787 s). Under heavy saturation (N=6) the two converge
(E2E p90 ≈ 118–120 s), since there is no slack for bursts to harm. Decode (TPOT)
is dispatch-independent everywhere.

**Recommendation:** benchmark with `--dispatch-mode thinktime`; use `tracets`
only as an explicit bursty stress case. Full ablation:
[`v2/exp_c_dispatch_ablation/`](v2/exp_c_dispatch_ablation/).

### How to use it

```bash
# 1. annotate a trace with the real per-turn gap (one-time; scans the raw trace)
python scripts/add_time_to_parent.py traces/w600_r0.0015_st30.jsonl traces/w600_ttp.jsonl

# 2. replay closed-loop with faithful think-time
python -m replayer --trace traces/w600_ttp.jsonl --endpoint <eps> \
    --model <model> --dispatch-mode thinktime
```

`time_to_parent_chat = this_turn.request_ready_time_ms − parent_turn.request_end_time_ms`,
computed from the raw trace and stored per request; turn-1 has none (fires at its
trace arrival). Traces without the field fall back to `tracets`.

---

## Project map

- [`PAPER_OUTLINE.md`](PAPER_OUTLINE.md) — GPU-hit-first paper outline (the thesis).
- [`v2/`](v2/) — evidence experiments:
  - `exp_a_tier_latency/` — KV-hit cost by tier (GPU < CPU-local < remote-RDMA < miss).
  - `exp_b_capacity_knee/` — realized APC / latency knee vs GPU capacity.
  - `exp_c_dispatch_ablation/` — the replay-mode study above.
- `replayer/` — trace replayer (`--dispatch-mode`, closed-loop think-time).
- `scripts/add_time_to_parent.py` — trace annotation for `thinktime`.
- `microbench/`, `analysis/` — PD-disagg, routing, workload characterization.
