club-3090/models/qwen3.6-27b

Qwen3.6-27B

Run Qwen3.6-27B — with vision and tool calling — on 1 or 2 RTX 3090s. Full OpenAI-compatible API, drop-in replacement for ChatGPT/Claude in any tool that uses the OpenAI SDK.

👉 For deployment options + workload-driven config picks, see the hardware-axis pages: docs/SINGLE_CARD.md (1× 3090) · docs/DUAL_CARD.md (2× 3090).

This page is the model-specific reference: quants, what's working / not working, VRAM allocation, engine pointers.

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What this is

• 27B parameter dense LLM with vision support (Qwen3-Next family — hybrid DeltaNet + standard attention)
• Quant on this stack: Lorbus/Qwen3.6-27B-int4-AutoRound — INT4 weights with BF16 mtp.fc head preserved (lets vLLM use MTP spec-decode)
• GGUF alternative: unsloth/Qwen3.6-27B-GGUF — Q3_K_XL ⭐ (validated by Marie's Kaitchup eval), Q4_K_M, Q5_K_S
• Engines: vLLM (full features) · llama.cpp (max context, lighter footprint) · ik_llama (best quality-per-bit GGUF) · SGLang (currently blocked, watch list)

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Quick start

The easiest entry is the wizard at the repo root, which asks engine + workload and boots the right compose:

bash scripts/setup.sh qwen3.6-27b
bash scripts/launch.sh

If you already know the variant you want, see docs/SINGLE_CARD.md or docs/DUAL_CARD.md for the menu, then:

bash scripts/switch.sh vllm/tools-text   # for example

Sanity check after boot:

curl -sf http://localhost:8020/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"qwen3.6-27b","messages":[{"role":"user","content":"Capital of France?"}],"max_tokens":200}'

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VRAM allocation across configs

How each config splits the 24 GB / card budget — weights, KV cache, vision tower, DFlash draft (where applicable), and activation/cudagraph headroom. Single-card (TP=1) on top, dual-card (TP=2, weights and KV halved across both GPUs) on bottom.

As of 2026-05-02 PM (vLLM v0.20 + Genesis v7.69 dev tip + local vllm#35975 backport + Cliff 2 60K closed), single-card recommended options (see docs/SINGLE_CARD.md):

• long-text.yml — 180K text-only, Balanced MTP at 0.93 mem-util. IDE-agent + steady-state default. Cliff 1 mech B + Cliff 2 60K both closed. KV pool ~284K tokens. AL 3.34-3.51, code 67 / narr 50 TPS.
• long-text-no-mtp.yml — 200K text-only, Max-context at 0.95 mem-util (NEW). MTP off; trades ~30% decode TPS for the larger KV pool. Same Cliff 2 60K closure. Use when one-shot input >50K matters more than steady-state throughput.
• long-vision.yml — 145K + vision at 0.95 mem-util. Vision tower's persistent ~1 GB tightens activation budget further than long-text. Same v7.69 patch stack.
• bounded-thinking.yml — 180K text-only + structured-CoT grammar in reasoning at 0.95 mem-util. Same patch stack as long-text plus --structured-outputs-config.enable_in_reasoning true. ~30× cheaper think output on coding workloads with +4.3pp HE+ / +24pp LCB v6 vs FREE thinking. See docs/STRUCTURED_COT.md.
• llamacpp/default — 262K + vision at ~21 TPS. Different engine, no cliffs anywhere — production-safe for unpredictable inputs.

The remaining shipped limitation on the vLLM single-card variants: single prompts >60K still hit the 24 GB hardware-physical wall. Use llama.cpp single (262K, slower) or dual-card TP=2 (262K, splits state across cards) for one-shot big prompts. See docs/CLIFFS.md.

Other variants (tq3-mtp.yml 48K · tools-text.yml 75K FP8 · minimal.yml 32K) are kept in the repo as fallbacks / diagnostics, not promoted as primary.

TP=2 unlocks 262K + 4 concurrent streams on dual-card (dual.yml).

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What's working

• Vision — images in messages via OpenAI-compat format. Tower is small (~0.5–1.0 GB VRAM); each image consumes 640–1280 tokens at default resolution. Quality is good for charts / screenshots / natural images, less reliable for OCR on dense text. No image generation — this model is vision-input-only.
• Tool calling — tools=[...] + tool_choice="auto", parsed cleanly into tool_calls[]. Genesis v7.62.x ships PN11 (Quentin-M's streaming-tool-call IndexError fix from vllm#41142).
• Streaming — SSE chunks add up to coherent text; tool-call deltas stream too.
• Reasoning mode — chat_template_kwargs.enable_thinking=true for chain-of-thought (vLLM extracts into reasoning_content field; llama.cpp emits inline).
• Spec-decode — MTP n=3 default on vLLM (~83% per-position-1 accept on code); DFlash N=5 on dual-card for code-heavy workloads.
• All standard sampling — temperature, top_p, top_k, repetition_penalty, JSON-mode, structured output.

What's not working today

• GGUF on vLLM for Qwen3-Next family — not supported upstream. Use llama.cpp for GGUF on this model.
• EAGLE spec-decode on hybrid attention — DeltaNet rollback issue (cross-engine architectural). Watch upstream.
• Single-card single prompts >60K — still hit the 24 GB hardware-physical wall. Cliff 2 closed at 60K (2026-05-02 PM) via Genesis v7.69 (PN32 GDN chunked-prefill + P103 worker self-install) + local vllm#35975 backport. Cliff 1 mech B (inductor compile-path FFN intermediate buffer leak) — closed since 2026-05-02 AM via PN25 v3 import-time backport + PN30 dst-shaped temp fix (now native in v7.69). For >60K single prompts: dual-card TP=2 (verified 237K) or llama.cpp single (262K). See docs/CLIFFS.md.

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Quant decision

Why AutoRound INT4 over alternatives:

Quant	Stack support	Bench	Trade-off
Lorbus AutoRound INT4 ⭐	vLLM --quantization auto_round	51-89 TPS depending on config	+9% over AWQ (this model); BF16 MTP head preserved; required for MTP spec-decode
AWQ INT4	vLLM --quantization awq	38 TPS @ 8K	Works; slower; no spec-decode advantage
GPTQ INT4 (palmfuture)	vLLM --quantization gptq	137 TPS @ 262K dual-card	Older path; AWQ + DFlash had a pad-Marlin × aux-layer bug we never reduced
GGUF Q3_K_XL (Unsloth dynamic)	llama.cpp only	21 TPS @ 262K	One Docker line, no patches, no Cliff 1/2; ⭐ Marie's Kaitchup eval picks this as optimal accuracy/footprint
GGUF Q4_K_M	llama.cpp only	~28 TPS measured 2026-04-23 (regression to ~21 today on current commit)	Heavier than Q3_K_XL; quality close

For deeper rationale, comparison tables, and the patched-vLLM-source story (vllm#40361 Marlin pad-sub-tile-n), see INTERNALS.md.

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Genesis patch surface (vLLM)

The vLLM composes mount Sandermage's Genesis tree and apply specific patches at boot. Currently pinned at commit 2db18df (v7.69 dev tip, 2026-05-02 PM) per scripts/setup.sh.

Active patches per compose (selected highlights — full env-var stack in each compose YAML):

Patch	What it does	Composes
P4	Hybrid TurboQuant support	TQ3 paths
P64	Streaming tool-call edge case	all single-card vLLM with MTP
P65	TurboQuant spec-CG downgrade (#40880 fix)	TQ3 paths
P66	Cudagraph capture-size divisibility	TQ3 paths
P67	TQ multi-query kernel	TQ3 paths
P68/P69 (50K char threshold)	Long-ctx tool-choice nudge — safe on real IDE-agent prompts since v7.65 raised default 8000 → 50000	TQ3 paths
P98	TQ WorkspaceManager revert (auto-skips on v0.20 due to drift marker; covered by Genesis PN34 since v7.69 — env-gate GENESIS_ENABLE_PN34_WORKSPACE_LOCK_RELAX=1)	TQ3 paths
P101	TQ continuation 64-token slicing	TQ3 paths
P103	FLA Cliff 2 chunked fwd_h+fwd_o orchestrator	TQ3 paths
PN12	FFN intermediate scratch pool — closes Cliff 1 mech B on TQ3 (Genesis-native on v0.20, no sidecar needed)	TQ3 paths
PN17	FA2 softmax_lse runtime clamp — closes Cliff 1 mech A	TQ3 paths
PN13	CUDAGraphWrapper lambda-arity (vllm#41235 backport)	all
P38B	In-source _continuation_prefill hook — Genesis #14 fix for compile-path silent no-op	TQ3 paths
P15B	FA varlen max_seqlen_k clamp at TQ wrapper — Genesis #15 fix	TQ3 paths
PN26b	First public sparse-V Triton kernel for SM86 (Ampere consumer) — 27B-tuned BLOCK_KV=8 num_warps=4 threshold=0.01	TQ3 paths
PN30 part3	DS conv state row-stride fix (replaces our dst-shaped sidecar, native in v7.69)	TQ3 paths
PN32	GDN chunked-prefill threshold + chunk size (env-vars PN32_GDN_*) — partial Cliff 2 mitigation	TQ3 paths
P103	FLA Cliff 2 chunked fwd_h+fwd_o orchestrator with worker self-install (v7.69 fix)	TQ3 paths
PN34	Workspace-lock relax env-gate (PN34_WORKSPACE_LOCK_RELAX=1) — replaces our local sidecar	TQ3 paths
PN8	MTP draft online-quant propagation (vllm#40849) — closes Cliff 1 on FP8 path	fp8 path (tools-text.yml)

Two local sidecars apply outside Genesis (post-v7.69):

• patch_inputs_embeds_optional.py (NEW 2026-05-02 PM) — backport of vllm#35975: skip inputs_embeds GPU buffer for text-only models, frees ~444 MiB on our 180K + MTP K=3 path. Two-site regex text-patch on gpu_model_runner.py and llm_base_proposer.py. Drop when upstream merges.
• patch_tolist_cudagraph.py — guards .tolist() calls during cudagraph capture. Unblocks TurboQuant KV + spec-decode + chunked-prefill on Qwen3-Next dense on Ampere. Drop when upstream fixes the continuation-prefill .tolist() sync.

Local sidecars dropped during the v7.69 cutover (2026-05-02 PM):

• patch_pn25_genesis_register_fix.py → covered by v7.69 worker-spawn registration
• patch_pn30_dst_shaped_temp_fix.py → covered by v7.69 PN30 part3
• patch_workspace_lock_disable.py → covered by v7.69 GENESIS_ENABLE_PN34_WORKSPACE_LOCK_RELAX=1

Earlier sidecars dropped during 2026-05-01 v0.20 migration:

• patch_pn12_ffn_pool_anchor.py → covered natively by PN12 on v0.20
• patch_pn12_compile_safe_custom_op.py → covered by Genesis PN25
• patch_fa_max_seqlen_clamp.py → covered by PN17 + P15B

Dual-card composes (dual.yml, dual-dflash*) are Genesis-less by design — fp8 KV + TP=2 + 0.92 mem-util has plenty of headroom and doesn't trigger the cudagraph bugs Genesis was built to patch. dual-turbo.yml does mount Genesis (TQ3 path needs P65).

Forensic chain + per-patch attribution → INTERNALS.md.

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See also

• /docs/SINGLE_CARD.md — 1× 3090 deployment menu (workloads → composes → TPS).
• /docs/DUAL_CARD.md — 2× 3090 deployment menu.
• INTERNALS.md — engineering deep dive (Genesis patches, forensics, Marlin pad, DFlash, upstream tracker).
• CHANGELOG.md — dated history (combines single + dual timelines).
• /docs/EXAMPLES.md — Python / TS / curl client snippets + Open WebUI / Cline / Cursor connection settings.
• vllm/ — vLLM-specific recipes (compose YAMLs are documented in their own headers).
• llama-cpp/ — llama.cpp recipes (max context on single card, no prefill cliffs).
• ik-llama/ — ik_llama.cpp recipes (IQK imatrix quants, best quality-per-bit).
• sglang/ — SGLang status (currently blocked).
• /docs/engines/ — cross-model engine comparison + per-engine deep dives.
• /docs/HARDWARE.md — hardware notes (Ampere, NVLink, power).