Qwen3.6-35B-A3B · vLLM · FP8 + MTP · conc 8

status: done

Configuration

ModelQwen/Qwen3.6-35B-A3B
CompanyAlibaba
FamilyQwen
Parameters35B / 3B (MoE)
EnginevLLM + MTP (speculative decoding)
Quant / precisionFP8
Why this quantQwen3.6-35B-A3B FP8 + the model's native MTP module (mtp.safetensors ships in-repo) — built-in multi-token-prediction speculative decoding on the sparse MoE.
DownloadQwen/Qwen3.6-35B-A3B-FP8
Context window65536
Input modalitiestext, image (served text-only here)

Measured results

Prefill tok/s260.87
Decode tok/s190.31
Peak memory (GB)104.3 (system MemAvailable delta (10s sampling) — vLLM static KV reservation (util 0.85) + MTP head)
Completed2026-06-22 20:11 +08

Full run command

# planned: base Qwen3.6-35B-A3B-FP8 + native MTP via vLLM --speculative-config (method finalized at
# run time; qwen3.6 ships mtp.safetensors in-repo).
docker run -d --gpus all --ipc=host -p 8000:8000 \
  -v ~/.cache/huggingface:/root/.cache/huggingface --env HF_TOKEN=*** \
  vllm/vllm-openai:cu130-nightly Qwen/Qwen3.6-35B-A3B-FP8 \
  --host 0.0.0.0 --port 8000 --max-model-len 65536 --gpu-memory-utilization 0.85 --max-num-seqs 8 \
  --speculative-config '{"method":"mtp","num_speculative_tokens":3}'

qwen3.6-35b-a3b Alibaba Qwen FP8 16-40B conc-8

Notes

conc-8 point of the Qwen3.6-35B-A3B MTP sweep — fastest per-token latency of any MTP run, acceptance holds. The sparse MoE (3B active) + native MTP at concurrency 8 (500-prompt / 300 s cap).

  • Workload: ShareGPT V3, concurrency 8. 228/500, 0 errors (hit the 300 s cap).
  • Per-stream latency: TPOT median 35.2 ms (≈28 tok/s/stream) — about half the dense Qwen3.6-27B MTP’s 66 ms at the same conc-8, because the MoE only activates ~3B params/token. TTFT median 355 ms.
  • Acceptance: ~68%, mean length 3.0 (per-position 0.85 / 0.68 / 0.53) — holds vs the conc-32 run’s 67%, confirming acceptance is workload-driven (the same ShareGPT MTP head hits ~67–70% at every concurrency on both the 27B dense and this 35B MoE).
  • Aggregate decode 190 / prefill 261 are concurrency-scaled (8 streams), lower than the conc-32 run (408 / 420) — not a regression; TPOT above is the meaningful low-concurrency metric. The MTP speedup vs no-MTP at conc 8 needs a matched base-at-conc-8 (deferred).