feat(qwen36-vl): swap FP8→NVFP4 + GPU1 rebalance (granite restored)
The nvidia ModelOpt NVFP4 MoE that failed on vLLM 0.19.1/0.22.0 (#44081) loads clean on 0.23.0. Cut prod qwen36 FP8→NVFP4: ~20.4 GiB weights vs ~34 (~40% lighter, ~13 GB reclaimed on GPU 1), faster single-stream on Blackwell FP4 cores, vision tower preserved (comfy-dev real anatomy-judge A/B on 16 prod images: PASS; brokkr text/speed: parity bar a minor multi-step-chained-reasoning slip that doesn't bite the judge role). - compose: pin image by 0.23.0 digest, drop --kv-cache-dtype fp8 (fp16 KV — the freed room buys full-precision KV), util 0.46→0.32. - GPU1 rebalance (pinned): granite restored 0.24→0.34 / 65536→131072 (undoes the FP8-era sacrifice); trio unchanged; total ~0.82, ~24 GB free. - gateway model name qwen3.6-35b-a3b unchanged (now NVFP4 behind it); thinking-split (enable_thinking=false default) intact — the judge needs it.
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@@ -1,34 +1,35 @@
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# Qwen3.6-35B-A3B VL (official FP8) on ana-ml2 — copy to .env on the host and fill.
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# Qwen3.6-35B-A3B VL (official NVFP4) on ana-ml2 — copy to .env on the host and fill.
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# Real .env lives on ana-ml2 at /opt/docker/compose/qwen36-vl/.env (gitignored).
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#
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# Replaces qwen35-vl (Qwen3.5-9B) 2026-06-14. See compose.yaml header for the
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# FP8-over-NVFP4 rationale (vLLM NVFP4 MoE loader broken, #44081) and why this
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# uses plain :latest with NO --quantization (pre-quantized checkpoint; a forced
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# flag would noise-quantize the vision tower like the old qwen35-vl).
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# Swapped FP8→NVFP4 2026-06-15 (the nvidia ModelOpt NVFP4 MoE now loads on vLLM
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# 0.23.0 — #44081 fixed). See compose.yaml header for the full rationale + the
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# comfy-dev vision A/B that cleared it + the GPU-1 rebalance.
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# :latest is fine — the official FP8 checkpoint loads + serves vision correctly
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# on 0.19.1 (validated 2026-06-14). NO pinned nightly digest needed.
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QWEN_IMAGE=vllm/vllm-openai:latest
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# PINNED by digest — NVFP4 needs vLLM >= 0.23.0 (the related ModelOpt NVFP4 MoE
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# path broke on 0.19.1/0.22.0). Pin guards against a :latest regression. This
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# digest = 0.23.0, validated to load + serve this checkpoint (vision incl.).
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QWEN_IMAGE=vllm/vllm-openai@sha256:6d8429e38e3747723ca07ee1b17972e09bb9c51c4032b266f24fb1cc3b22ed8f
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QWEN_CONTAINER_NAME=vllm-qwen36
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QWEN_MODEL=Qwen/Qwen3.6-35B-A3B-FP8
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QWEN_MODEL=nvidia/Qwen3.6-35B-A3B-NVFP4
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QWEN_PORT=8007
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# GPU 1 = shared with the granite summarizer + embed/rerank/reward trio.
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# GPU 0 is kept free for the llama-swap creative-writing hot-swap card.
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# (GPU 0 now hosts Mistral Small 4, not the old llama-swap card.)
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QWEN_GPU_ID=1
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# util 0.42 (~40 GB) — official FP8 weights load in ~34.2 GiB; 0.42 covers
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# weights + CUDA-graph + a generous KV pool. Hybrid attn (10 of 40 layers full-
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# attn, ~10 KB/tok KV) makes long context nearly free, so max-len is generous.
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# Budget (2026-06-14): qwen36 0.42 + granite 0.28 + trio 0.20 = 0.90 total,
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# ~10 GB graph headroom. Bring qwen36 up LAST so capture sees the free room.
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QWEN_GPU_MEM_UTIL=0.42
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# util 0.32 (~31 GB) — NVFP4 weights load in ~20.4 GiB; 0.32 covers weights +
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# fp16 KV + CUDA-graph. fp16 KV (compose drops --kv-cache-dtype fp8): the NVFP4
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# swap freed enough room to run full-precision KV. Hybrid attn (10/40 full-attn)
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# keeps even fp16 KV cheap. GPU-1 budget (2026-06-15 rebalance, pinned): qwen36
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# 0.32 + granite 0.34/131072 (RESTORED from the FP8-era 0.24/64K) + trio 0.16 =
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# ~0.82, ~24 GB free headroom. Recreate ONE service at a time (profiling race).
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QWEN_GPU_MEM_UTIL=0.32
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QWEN_MAX_MODEL_LEN=131072
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# Sampler-warmup OOM guard on the shared GPU (248K vocab × default 1024 seqs is
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# a huge transient). 32 is plenty for a vision endpoint.
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QWEN_MAX_NUM_SEQS=32
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# Optional
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# Optional — checkpoint is ungated.
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HF_TOKEN=
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API_KEY=
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@@ -1,19 +1,22 @@
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# qwen36-vl — Qwen3.6-35B-A3B vision-language MoE (official FP8) on ana-ml2.
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# qwen36-vl — Qwen3.6-35B-A3B vision-language MoE (official NVFP4) on ana-ml2.
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#
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# Replaces the qwen35-vl stack (Qwen3.5-9B) 2026-06-14. Co-located on GPU 1 with
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# the granite summarizer + embed/rerank/reward trio (GPU 0 stays free for the
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# llama-swap creative-writing hot-swap card). Serves on :8007.
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# the granite summarizer + embed/rerank/reward trio. Serves on :8007.
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#
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# WHY official FP8 (not NVFP4): NVFP4 (nvidia/Qwen3.6-35B-A3B-NVFP4, ~21 GB) is the
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# lighter fit but its vLLM ModelOpt-MoE loader is BROKEN as of 0.19.1/0.22.0
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# (KeyError w2_input_scale / lm_head.input_scale — vLLM #44081). The official
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# Qwen pre-quantized FP8 (~34 GB weights) loads clean on :latest and — unlike
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# the old qwen35-vl — needs NO pinned nightly digest: that hack existed because
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# vLLM DYNAMIC `--quantization fp8` quantized the vision tower to noise. This
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# checkpoint is PRE-quantized, so we OMIT --quantization (vLLM auto-detects the
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# checkpoint's own fp8) and the vision tower is preserved. Validated 2026-06-14
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# on GPU 0: loads in 34.2 GiB, image test returns correct ("Blue"). Revisit
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# NVFP4 (frees ~13 GB) once vLLM's loader is fixed.
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# WHY NVFP4 now (swapped FROM FP8 2026-06-15): the nvidia ModelOpt NVFP4 MoE that
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# was BROKEN on vLLM 0.19.1/0.22.0 (#44081, lm_head.input_scale) loads clean on
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# 0.23.0 — the ModelOpt lm_head fix landed. So we cut FP8→NVFP4: ~20.4 GiB weights
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# vs FP8's ~34 GiB (~40% lighter, ~13 GB reclaimed on GPU 1), faster single-stream
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# on Blackwell's FP4 tensor cores, and the freed room funds fp16 KV + a granite
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# context restore (see the rebalance note below). The NVFP4 checkpoint preserves
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# the vision tower (ModelOpt leaves it high-precision) — VALIDATED by comfy-dev's
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# real anatomy-judge A/B on 16 prod images: PASS, holds the load-bearing
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# discrimination (gross-deformity reject + clean-pass), only shuffles already-
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# unreliable sub-ceiling borderline-hand calls. brokkr's text/speed arm: parity
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# except a minor multi-step chained-numeric-reasoning slip (W4A4 tell) — doesn't
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# bite the vision-judge role; flag for any gateway consumer doing chained math.
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# Requires vLLM >= 0.23.0 (pinned by digest in .env). NO --quantization flag
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# (vLLM auto-detects the checkpoint's NVFP4).
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#
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# NAMING: served ONLY as its TRUE name `qwen3.6-35b-a3b`. A model is never aliased
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# under a prior model's name — a caller asking for `qwen3.5-9b-fp8` (a 9B dense)
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@@ -22,10 +25,14 @@
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# hero-judge, stacks/arbo v0.11.3+) migrate to `qwen3.6-35b-a3b` — they 404 on the
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# old name until they repoint, which is the correct loud signal (notified 2026-06-14).
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#
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# WHY util 0.42 / max-len 131072: hybrid attn (10 of 40 layers full-attn, ~10 KB/
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# tok KV) → KV is cheap, so big context is nearly free; the 34 GB weights are the
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# cost. 0.42 (~40 GB) = weights + graph + generous KV. Granite drops to 0.25/64K
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# to make room (the FP8-vs-maxed-granite tradeoff, operator-approved 2026-06-14).
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# GPU-1 REBALANCE (2026-06-15, pinned): the NVFP4 swap freed ~13 GB, redistributed —
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# qwen36 NVFP4: util 0.46→0.32 (~31 GB: 20.4 GB weights + fp16 KV + graph).
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# fp16 KV (we DROPPED --kv-cache-dtype fp8) — the freed room buys back full-
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# precision KV; hybrid attn (10/40 full-attn) keeps even fp16 KV affordable.
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# granite: RESTORED 0.24→0.34, max-len 65536→131072 (gives back the context
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# sacrificed for FP8 qwen — the FP8-vs-maxed-granite tradeoff is now undone).
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# trio (embed/rerank/reward) unchanged at floor.
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# Total GPU-1 util ~0.82 → ~17 GB headroom (was a tight ~5 GB).
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#
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# THINKING TOGGLE: this is ONE hybrid checkpoint (not separate Instruct/Thinking
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# downloads) with a Qwen3-style per-request `enable_thinking` switch. The chat
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@@ -60,8 +67,8 @@ services:
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- VLLM_API_KEY=${API_KEY:-}
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command:
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- ${QWEN_MODEL}
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# Pre-quantized FP8 checkpoint → NO --quantization (vLLM auto-detects; a
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# forced flag would re-quantize the vision tower to noise, see header).
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# Pre-quantized NVFP4 (ModelOpt) checkpoint → NO --quantization (vLLM auto-
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# detects; the vision tower is left high-precision by the producer).
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- --served-model-name
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- qwen3.6-35b-a3b
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- --host
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@@ -78,8 +85,8 @@ services:
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# vision endpoint (the summarizer carries the concurrency, not this).
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- --max-num-seqs
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- ${QWEN_MAX_NUM_SEQS}
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- --kv-cache-dtype
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- fp8
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# fp16 KV (no --kv-cache-dtype): the NVFP4 swap freed enough room to run
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# full-precision KV — better than the fp8 KV the FP8 build needed to fit.
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- --trust-remote-code
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- --dtype
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- auto
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@@ -110,9 +117,9 @@ services:
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- tnet
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labels:
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- homepage.group=AI Systems
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- homepage.name=Qwen3.6-35B-A3B VL (FP8)
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- homepage.name=Qwen3.6-35B-A3B VL (NVFP4)
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- homepage.icon=mdi-image-search
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- homepage.description=Qwen3.6-35B-A3B vision-language MoE (FP8) via vLLM (ana-ml2)
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- homepage.description=Qwen3.6-35B-A3B vision-language MoE (NVFP4) via vLLM (ana-ml2)
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- homepage.href=http://10.250.50.54:${QWEN_PORT}/docs
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networks:
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