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lm_head, the vision tower, the DeltaNet conv1d, and the MTP head. Everything else is NVFP4 W4A4.--quantization flag.device_map=auto): 122 seconds end to end.| concurrency | aggregate t/s |
|---|---|
| 1 | 69.0 |
| 4 | 202.3 |
| 8 | 377.8 |
1vllm serve sakamakismile/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-NVFP4 \
2 --trust-remote-code --tensor-parallel-size 4 \
3 --max-model-len 32768 --kv-cache-dtype fp8 --reasoning-parser qwen3 \
4 --speculative-config '{"method":"qwen3_5_mtp","num_speculative_tokens":3}'NCCL_P2P_DISABLE=1 and --disable-custom-all-reduce.reasoning_effort: "medium" for anything long-form. At the default (highest) effort this model can spend its entire token budget inside <think> — the reasoning block grows past 18,000 characters, sometimes degenerating into repeating a single line, and no answer is ever emitted. At medium effort the same prompts finish cleanly every time: I ran a 9-case gate (French and English long-form, temperature 0 and 0.7) in three configurations — NVFP4 with MTP num_speculative_tokens: 6, NVFP4 without MTP, and the original bf16 checkpoint with MTP — and all 27 runs passed with thinking held to 1–3k characters."chat_template_kwargs": {"reasoning_effort": "medium"}mtp.* modules are listed in quantization_config.ignore — do not remove them. If vLLM treats the bf16 MTP head as NVFP4 the draft breaks silently: 0% acceptance, slower than no MTP at all.gptq_marlin_repack: size_n=24 not divisible by tile_n_size=64). W4A4 only.targets: [Linear], ignore: [lm_head, re:.*visual.*, re:.*conv1d.*, re:.*mtp.*], 32 calibration samples × 8192 tok from neuralmagic/calibration. MTP tensors grafted back in bf16 after saving and appended to quantization_config.ignore.