# Sparse MoE decode — 1.8× over dense; beats llama.cpp at TP=2 (gpt-oss-20b, RTX 5090)

Phase 20 (`docs/20-sparse-moe.md`): decode computes only the routed top-4
experts via fused expert-indexed GEMVs (`csrc/moe/moe_sparse.cu`) instead of
the dense all-local-expert batched GEMM. FP8 weights run W8A16 (weights FP8,
activations BF16 — decode is memory-bound, tensor cores irrelevant at M=1);
MXFP4 runs W4A16. Dense path retained for prefill / `num_tokens > 8` and via
`XSERV_DENSE_MOE=1` for A/B.

## In-process decode (bench-gpt-oss, greedy, 96 tokens)

| config | TPOT | tok/s |
|---|---|---|
| dense FP8 TP=2 (baseline) | 13.9 ms | 72 |
| **sparse FP8 TP=2** | **7.6 ms** | **132** |
| sparse MXFP4 TP=2 | 8.4 ms | 118 |
| sparse FP8 TP=1 (one 5090) | 7.8 ms | 128 |
| sparse MXFP4 TP=1 | 8.9 ms | 113 |

- Sparse FP8 = **1.8× over dense**. Greedy output stays coherent.
- TP=1 ≈ TP=2: expert reads are now so small that PCIe all-reduce eats the
  TP gain — single-GPU serving becomes the attractive deployment.
- MXFP4 reads half the bytes of FP8 but stays slower: the 4-bit dequant GEMV
  has lower effective bandwidth (same fixed inefficiency seen in the dense
  MXFP4 experiments); at sparse sizes both are partly launch/latency-bound.

## Head-to-head vs llama.cpp (tools/xserv_vs_llama.py, warm servers, TP=2, GPUs 0-1, 6 reps, 256 tok)

| prompt | metric | xserv sparse FP8 | llama MXFP4 | xserv vs llama |
|---|---|---|---|---|
| short | TTFT | **35.3 ms** | 62.7 ms | 1.78× faster |
| short | TPOT | **7.32 ms** | 8.42 ms | 1.15× faster |
| medium | TTFT | **49.4 ms** | 65.0 ms | 1.32× faster |
| medium | TPOT | **7.19 ms** | 7.54 ms | 1.05× faster |
| medium | tok/s | **139.1** | 132.7 | |
| long (1.6k) | TTFT | 94.1 ms | **44.7 ms** | 0.48× (llama wins) |
| long | TPOT | **7.25 ms** | 7.64 ms | 1.05× faster |

**Decode TPOT now beats llama.cpp at every prompt length** (was 2× slower:
13.1 vs 6.6 ms before sparse). Remaining loss: long-prompt TTFT — prefill is
still the dense all-expert GEMM; sparse/grouped prefill is the next phase.

**Post-review fixes** (same harness, rerun): removing three leftover
`cudaDeviceSynchronize` from the decode hot path and replacing the CPU-tiled
prefill bias-add (96 D2H/H2D round-trips per prefill) with a GPU broadcast
kernel improved both axes — TPOT 7.19-7.32 → **6.99-7.21 ms**, TTFT
short/medium/long 35/49/94 → **29/42/79 ms**. GSM8K-50: 94% (unchanged).

## TP=1 head-to-head (single 5090; server now routes gpt-oss tp=1 to the TP engine)

| prompt | metric | xserv sparse FP8 | llama MXFP4 |
|---|---|---|---|
| short | TTFT / TPOT | 42.8 ms / 7.00 ms | **34.5 ms / 3.22 ms** |
| medium | TTFT / TPOT | 57.1 ms / 7.19 ms | **37.3 ms / 2.89 ms** |
| long | TTFT / TPOT | 119.6 ms / 7.20 ms | **27.8 ms / 2.88 ms** |
| | tok/s | 139–143 | **311–347** |

**Single-GPU is llama.cpp's sweet spot and it wins 2.2–2.5×.** Two structural
reasons, both instructive:

1. llama TP=2 (7.5–8.4 ms) is much WORSE than its TP=1 (2.9 ms): its PCIe
   cross-GPU split costs ~5 ms/token. xserv's NCCL all-reduce is cheap enough
   that TP=2 ≈ TP=1 (7.2 vs 7.0 ms) — but xserv's single-GPU floor is high.
2. xserv TP=1 reads ~4.7 GB/token (experts FP8 2.4 GB + **non-expert weights
   still BF16** ~2.3 GB, half of that the 201k-vocab lm_head) ≈ 3.1 ms of pure
   HBM time; the other ~4 ms is launch overhead (~200 kernels/token, no CUDA
   graphs) + BF16 GEMV efficiency. llama reads ~1.3 GB (everything MXFP4) and
   replays the whole token as one CUDA graph.

## Correctness

- Greedy generations coherent across prompts (FP8/MXFP4, TP=1/2).
- Sparse FP8 is W8A16 vs dense W8A8 — activations are no longer quantized, so
  tokens are not expected to be byte-identical to dense; quality is checked by
  GSM8K instead.
- **GSM8K-100 (greedy, TP=2, `tools/eval_gsm8k_fast.py`): 96/100 = 96.0%** vs
  dense FP8 91.0% / BF16 90.0% — no regression (within greedy-nondeterminism
  noise; W8A16 removes activation-quantization error so ≥ dense is expected).
  Avg 1.3 s/problem also reflects the decode speedup.

## Remaining gaps / next levers (to catch llama TP=1 at 2.9 ms)

Sparse MoE removed the dominant cost; the residual ~7 ms splits roughly into
~3 ms HBM reads and ~4 ms fixed overhead. In impact order:

1. **CUDA graphs for decode** (~2–4 ms): with experts down to ~1–2 ms, the
   ~200 un-graphed launches/token are now the single largest cost. (The old
   "graphs ≈ useless" conclusion was relative to a 13 ms dense TPOT — no
   longer true.)
2. **Quantize non-expert weights** (~1–1.5 ms): attn qkv/o + the 1.16 GB BF16
   lm_head read every token; FP8/MXFP4 them like llama quantizes everything.
3. **Sparse prefill** (permute tokens by expert + grouped GEMM): long-prompt
   TTFT 94–120 ms → llama's ~30 ms territory.
4. **W4A4 FP4 tensor cores / bandwidth-tuned MXFP4 GEMV**: make 4-bit experts
   actually beat FP8 (today sparse MXFP4 is 8.4 ms vs FP8 7.6 ms — the 4-bit
   GEMV's lower effective bandwidth still cancels its byte advantage).