qwen3.6-windows-server

One-click Qwen3.6-27B inference on Windows. Unzip, double-click, you're serving on http://127.0.0.1:5001/v1. No WSL, no Docker, no conda, no pip, no admin. Everything runs on your machine. No telemetry. No analytics. No phone-home.

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What this is

A small portable Windows app that gives you an OpenAI-compatible API serving Qwen3.6-27B locally, with config presets that I actually measured myself. The launcher is a Textual TUI: arrow keys, Enter to start a snapshot, Esc to stop. Press e to add, edit, duplicate, or delete your own snapshot configs from inside the TUI, no hand-editing files. That's the whole UX.

It is the matching launcher for the devnen/vllm-windows patched wheel, but you don't need to know or care about that. The wheel ships inside the launcher zip.

What you get

On a single RTX 3090 (24 GB), running Lorbus AutoRound INT4:

Every snapshot below has the tool-calling fix baked in (PR #35687 + #40861 + qwen3.5-enhanced.jinja + preserve_thinking=false), so any one of them works with any OpenAI-compatible client, Claude Code, Cline, Cursor, Codex, OpenCode, KiloCode, LM Studio, etc. Just point it at the listed port.

Snapshot	Decode tok/s	Prompt class	Context	Use it when
start_72tps	~72	short (~200 tok)	32 k	Short-prompt / chat baseline. MTP n=3.
start_speed	64.5	long (100 KB)	90 k	Default for long prompts. MTP n=6, see note below.
start_127k	53.4	long (100 KB)	127 k	Maximum context on a single 3090.
start_mtp4	58.3	long (100 KB)	120 k	Mid-balance speed vs context.
start_pp2_160k (2 GPU)	43.5	long (100 KB)	160 k	Pipeline-parallel for the largest contexts.
start_gpu0_50k	56.9	mixed	9–50 k	Single-GPU + display, fallback when you can't boot-quiet.

GPU index note. start_72tps, start_speed, start_127k, and start_mtp4 pin to GPU 1 so GPU 0 stays free for the desktop compositor and other apps on a 2× 3090 box. On a single-GPU host the snapshot detects that via nvidia-smi and falls back to GPU 0 with a warning. start_pp2_160k requires two GPUs.

Single 3090 with display attached. You can run the full start_speed snapshot at 90 k context if you close heavy GPU apps (Chrome, Discord, Slack, video playback) during boot. Once vLLM has reserved its KV pool, the driver schedules everything else around what vLLM already owns, so you can reopen those apps and they'll behave normally. The danger is reopening them before boot finishes, mid-allocation OOM is what kills runs. If you can't or won't boot-quiet, start_gpu0_50k is the conservative fallback (mem_util 0.92, ~50 k ctx, same decode tok/s).

Long-prompt rows were measured on a ~100 KB / ~24 k-token Python source-summary prompt fed to windows_tools\bench_summarize.py. From v1.3.3 the shipped fixture is CPython 3.12's Lib/inspect.py (~130 KB, ~25 k tokens, PSF-licensed) so anyone can reproduce these numbers from a clean install, windows_tools/bench_prompt_sample.py is the file. The short-prompt row was measured on a ~200-token chat turn via windows_tools\bench.py. All numbers coherence-validated, TPS without coherence is a lie.

Why MTP n=6 on start_speed? n=3 is the universal short-prompt sweet spot and ships as start_72tps. On long, dense Python source the acceptance curve shifts later, n=6 won my coherence sweep (n=3 / 4 / 5 / 6 / 7 / 8 → 53.4 / 58.3 / 62.8 / 64.5 / 61.5 / 58.0 tok/s; full sweep in docs/TUNING.md). Always re-sweep on a representative prompt for your workload.

Honest framing: these are not r/LocalLLaMA records. Community has hit 80–82 tok/s on a 3090 with TurboQuant 3-bit KV, and 160 tok/s on a 5090. The unique angle here is native Windows, no WSL. Same recipe, no virtualization tax, one community member measured the same hardware going from 85 tok/s in WSL to 160 tok/s in native Ubuntu (reported here). This launcher closes that gap on Windows.

Community Linux / Blackwell validation

An independent Linux run on an RTX PRO 5000 Blackwell 48 GB card validated the same Qwen3.6-27B NVFP4 direction at 256K context with vLLM 0.20.2, FlashInfer 0.6.8.post1, ModelOpt NVFP4 weights, fp8_e4m3 KV cache, chunked prefill, and MTP.

Highlights from that reproduction:

Test	Result
47K health check	46,855 prompt tokens, 4/4 needles, roughly 5,800 tok/s estimated prefill
200K target	197,391 prompt tokens, 4/4 needles
256K stretch	252,510 prompt tokens, 4/4 needles after raising output budget
NVFP4 path	FlashInferCutlassNvFp4LinearKernel + Triton/FLA GDN prefill + FlashInfer attention
MTP n=3	87.8% acceptance, 97.8 tok/s engine decode
MTP n=6	78.2% acceptance, 120.9 tok/s engine decode

This is not a Windows launcher snapshot and it does not replace the 3090 numbers above. It is a Linux/Blackwell validation showing that the NVFP4 + fp8 KV path can run 200K-256K practical needle tests on a single 48 GB Blackwell workstation GPU without OOM. Full report and raw data: docs/pro5000-linux-nvfp4/.

Why this exists

Most fast Qwen3.6-27B recipes on r/LocalLLaMA assume Linux + Docker, or Linux-in-WSL. Windows users either pay the WSL tax, dual-boot, or skip inference entirely. None of those is great if your daily driver is Windows.

This launcher is the third option:

• Native Windows. Runs as a normal Windows process. No virtualization layer.
• Portable. Unzip the launcher, drop your model into a folder, double-click. That's it.
• Validated. Every config in here was measured against a coherence battery before being checked in. No copy-pasted Reddit recipes that look fast but emit * * * *.
• Local-only. No outbound calls except when you explicitly ask the launcher to download a model from HuggingFace. No telemetry of any kind, ever.

Install

TL;DR for CI / agents / scripted installs, one line, no TUI:

start.bat --auto-download --snapshot start_72tps

That installs the runtime, downloads the model if missing, and starts serving on http://127.0.0.1:5001/v1. See Headless / scripted install below for all the flags.

Hand the install to a coding agent, copy/paste prompt at docs/AGENT_INSTALL_PROMPT.md. Edit the one INSTALL_DIR line, paste into Claude Code / Cursor / Codex CLI / any agent with shell access, and it does the download + extract + runtime install + model fetch + smoke test end-to-end while you do something else.

Interactive path:

1. Download qwen3.6-windows-server-portable-x64.zip from the latest Release. Extract anywhere (no admin needed).
2. Double-click start.bat. The first run does two one-time steps, then drops you in the TUI:
   • Runtime install (~5–15 min, several GB). The bundled vLLM wheel + ~150 transitive deps (torch, CUDA wheels, transformers, etc.) install into the embedded Python's site-packages. A marker file is written so subsequent launches skip this entirely.
   • Model setup. Looks for Qwen3.6-27B-int4-AutoRound weights on your fixed drives (scans <drive>:\, _models\, models\, AI\, AI\models\, huggingface\, huggingface\hub\, models\Lorbus\). If it doesn't find them, offers to auto-download from Hugging Face (~16 GB, public, no token) or accepts a path to weights you already have. If your weights live somewhere else, pass --model-dir <path> to skip the scan.
3. Pick a snapshot, press Enter, you're serving on http://127.0.0.1:5001/v1.

The portable zip ships with an embedded Python 3.12 runtime, the patched vLLM wheel, the launcher TUI, a portable Windows Terminal, and a vendored get-pip.py. No conda, no system-Python install, no registry changes, no admin prompts. The runtime install on first run is the only network-dependent step besides the model download.

Don't have the model yet? See docs/MTP_HEAD.md, use the Lorbus AutoRound quant, the others won't draft.

Detailed install (including the wheel-only path for users who already have their own venv): docs/INSTALL.md.

Optional: install MSVC 2022 for the small decode boost

The launcher works on a vanilla Windows install, no MSVC required. But if you install Visual Studio 2022 Build Tools (free, no full IDE) with the "Desktop development with C++" workload, the snapshots auto-detect it and turn on vLLM's flashinfer sampler path, which JIT-compiles a faster top-k / top-p kernel on first launch.

What it costs:

• ~7 GB download, one-time install.
• Extra 30 to 60 s on the first profile_run of each new snapshot while the kernel compiles. Subsequent boots reuse the compiled cache.

What you get:

• A small but measurable decode boost on the sampler path.

Without MSVC, the snapshots transparently fall back to the PyTorch sampler, which never JIT-compiles anything. Boot is faster and the server is reliable; you just leave a few percent of decode tok/s on the table. The launcher prints a one-line [info] at startup telling you which path it picked.

Get the Build Tools installer here (official Microsoft aka.ms shortlink, pinned to VS 2022 / 17.x so it stays on the right product even after VS 2026 ships): https://aka.ms/vs/17/release/vs_buildtools.exe

ninja (the other half of the JIT toolchain) ships inside the launcher zip, you don't need to install it separately.

Test it

Once the server is up:

curl http://127.0.0.1:5001/v1/chat/completions ^
  -H "Content-Type: application/json" ^
  -d "{\"model\":\"any\",\"messages\":[{\"role\":\"user\",\"content\":\"Capital of France?\"}],\"max_tokens\":2000}"

Note the "model": "any", the patched wheel accepts any value. You don't have to know what the model is called.

Why max_tokens: 2000? Qwen3.6 is a thinking model: it spends the first chunk of its budget reasoning inside <think>...</think> and only then writes the answer to content. With max_tokens: 50 the entire budget gets eaten by the thinking phase and you'll see content: null plus finish_reason: "length", the server is fine, the budget was just too small. 1500–2000 is a safe floor for short Q&A.

Where's the answer in the response? The final answer lands in choices[0].message.content. The chain-of-thought lands in a separate choices[0].message.reasoning field, that's the --reasoning-parser=qwen3 wheel patch doing its job, not a bug. Most chat clients show content and ignore reasoning; if yours doesn't, point it at content.

If the request hangs, tail logs\vllm_server.<port>.log for vLLM's own stdout, the parent launcher logs only the boot banner; the serving process tees its progress to that file.

Headless / scripted install

End-to-end automated install (no TUI, no prompts), useful for CI, remote machines, agent installers, or just keeping a repeatable recipe:

start.bat --auto-download --snapshot start_72tps

The launcher runs the first-run setup (vLLM wheel + ~150 deps), auto-downloads the Lorbus quant from Hugging Face if it's missing, applies the tokenizer patch automatically, and execs the chosen snapshot, all without opening the TUI. Other useful flags:

start.bat --model-dir D:\models\Qwen3.6-27B-int4-AutoRound --snapshot start_speed
start.bat --headless     :: skip TUI, run the default snapshot (start_72tps)
start.bat --setup-only   :: install runtime + model, then exit (no serving)

--headless without --snapshot now runs the default snapshot (start_72tps) instead of exiting after setup checks. To run only the setup checks (the old --headless behavior), pass --setup-only.

The launcher also stays in the parent terminal, instead of detaching into a new Windows Terminal window, when it sees any of WT_SESSION, VLLM_NO_WT, CI, GITHUB_ACTIONS, MSYSTEM, or TERM in the environment. That covers GitHub Actions, git-bash, MSYS, agent runners, and anything that exports TERM. So your captured stdout won't go missing.

For benchmark numbers like the table above, use the bundled tools:

windows_tools\bench.bat              :: short prompt, decode-only TPS
windows_tools\bench_summarize.bat    :: ~100 KB / ~24 k-token prompt, prefill + decode + KV
windows_tools\check_coherence.bat    :: 3-tier coherence validator

Hardware reality

Tuned and measured on:

• Windows 10 Enterprise 22H2
• 2× NVIDIA RTX 3090 (Ampere sm_86), no NVLink, PCIe Gen 4
• 350 W power cap (250 W also benchmarked, see docs/TUNING.md)

Should also work on any Ampere or Ada NVIDIA GPU running Windows 10/11, 3090, 4090, A6000, etc. Will not work on Pascal, Turing, Intel Arc, or any AMD card. Single GPU with the display attached loses 1–3 GiB of VRAM to the desktop compositor and another 2–5 GiB to running apps, but you can still run the full start_speed snapshot at 90 k context by closing heavy GPU apps (Chrome, Discord, Slack, video playback) during boot, then reopening them after vLLM finishes booting. If you can't boot-quiet, fall back to start_gpu0_50k. Either path is covered in docs/WINDOWS_VRAM_HEADLESS.md.

RTX 50-series (Blackwell, 5060 / 5070 / 5080 / 5090): supported via the Blackwell zip. Download qwen3.6-windows-server-portable-x64-blackwell.zip instead of the default zip. It bundles vllm-0.20.0+cu132.devnen.2 against CUDA 13.2 / PyTorch cu130 with sm_120 kernels. v1.3.0 ships NVFP4 as the new default (rtx5090_nvfp4, port 5001) using the Peutlefaire/Qwen3.6-27B-NVFP4 weights. These route FFN GEMMs through FlashInfer's sm_120 native FP4 tensor cores, escaping the 170W prefill ceiling that AutoRound INT4 hits on consumer Blackwell. Measured on a single RTX 5090 at 575W: ~5,300 tok/s prefill @ 47k prompt (5x AutoRound), ~92 tok/s decode at 200k context. A second snapshot rtx5090_nvfp4_vision (180k ctx) ships as experimental for image and video input. As of v1.3.7, NVFP4 is the only supported 5090 path; the AutoRound INT4 5090 snapshots have been removed since they cannot escape the 170W ceiling on consumer Blackwell. NVIDIA driver 596+ required. See docs/BLACKWELL.md for the full story and docs/SM120_GDN_CEILING.md for the prefill-ceiling investigation.

If you're on a 4090, expect slightly higher numbers than mine. If you're on something more exotic, nothing here is going to work without your own tuning, that's fine, please share what you find.

Scope. This launcher serves Qwen3.6-27B specifically through a fixed set of validated snapshots. It is not a general vLLM server you can point at any model. Adding configs for smaller Qwen variants is straightforward (see docs/SNAPSHOTS.md); running unrelated models like ACE-Step, Stable Diffusion, or other diffusion / multimodal stacks is out of scope.

The local-AI ethos

Everything runs on your machine. No telemetry. No analytics. No phone-home. No cloud inference. No model weights downloaded behind your back. The launcher never opens an outbound connection except when you explicitly ask it to (downloading a model from HuggingFace via your own browser/huggingface-cli). This is in the spirit of r/LocalLLaMA: your hardware, your weights, your prompts, your business.

The launcher and every script are Apache-2.0. The bundled wheel inherits upstream vLLM's Apache-2.0 license. SHA256 of every release asset is published next to the release, verify before extracting.

What's under the hood

The wheel that powers this launcher is devnen/vllm-windows: a patched native-Windows build of vLLM, with three Windows-specific fixes (CPU-relay for Gloo collectives, Qwen3 reasoning-parser fix mirrored from PR #35687, hardwired wildcard model name). The full diff is at CHANGES_VS_SYSTEMPANIC.md in that repo. You don't have to download it separately, it's bundled inside this launcher's portable zip.

Documentation

• docs/INSTALL.md, full install + the bring-your-own-venv path.
• docs/UPGRADING.md, in-place updater (update.bat), preserve list, variant switching.
• docs/BLACKWELL.md, single landing page for RTX 50-series users.
• docs/MODELS.md, swapping in other quants / model sizes / Qwen variants.
• docs/CLAUDE_CODE.md, point Claude Code at the local server (native /v1/messages, no proxy).
• docs/CODEX.md, use OpenAI Codex CLI with this server (Responses API, developer-role template fix).
• docs/OPENCODE.md, point OpenCode at the local server (custom OpenAI-compatible provider, AGENTS.md path-handling rule).
• docs/QWEN_CLI.md, use Alibaba's official Qwen Code agent against this server.
• docs/PI.md, use the Pi coding agent with this server (custom provider extension, auto-compaction notes).
• docs/UNINSTALL.md, clean removal (it's portable, so just delete folders).
• docs/HARDWARE.md, what works, what doesn't, and why.
• docs/COMPARISON.md, how this stacks up against Ollama, LM Studio, llama.cpp, Docker, and WSL2.
• docs/COHERENCE.md, degenerate-output guide and the 3-tier validator.
• docs/TROUBLESHOOTING.md, every failure mode I've hit.
• docs/TUNING.md, the lever set, anti-levers, how to sweep your own configs.
• docs/MTP_HEAD.md, why Lorbus AutoRound is the only INT4 quant that works (the NVFP4 weights ship a separate MTP head and are documented in docs/BLACKWELL.md and docs/SM120_GDN_CEILING.md).
• docs/SPEC_DECODE_MATRIX.md, what spec-decode + parallelism combos work.
• docs/SNAPSHOTS.md, managing snapshots from inside the TUI: keyboard shortcuts, the CRUD editor, flag invariants, hand-edit fallback.
• docs/WINDOWS_VRAM_HEADLESS.md, free VRAM on Windows for single-GPU.
• docs/HALLUCINATED_FLAGS.md, flags from web search results that don't exist on this wheel.
• docs/CREDITS.md, vLLM team, SystemPanic, Lorbus, the community.

Contributing

Bug reports welcome, please include GPU model, driver version, Windows build, and the relevant slice of logs\vllm_server.<port>.log. The issue template walks you through it.

Share your configs. Each snapshot in snapshots/ is just a validated set of vLLM flags for one hardware/model combo, plus a card in launcher/configs.yaml so the launcher can list it. If you've got a config that runs coherent and faster (or with more context) than what's in here, please send a PR. The bar is the 3-tier coherence check, TPS without coherence won't be merged.

Configs I'd love to see:

• Other Qwen3.6-27B quants (FP8, additional NVFP4 variants, smaller AutoRound variants)
• Smaller Qwen models (14B, 8B, 4B) for 16 GB cards
• 4090 / 5090 / 5060 Ti / A6000 tunings
• New parallelism or KV-cache combos as vLLM adds them

How to add a snapshot: docs/SNAPSHOTS.md (in-TUI editor and hand-edit fallback).

This project is intentionally narrow scope: Windows + Ampere/Ada/Blackwell NVIDIA. PRs for other operating systems or GPU vendors are politely out of scope, please go upstream.

Credits

• vLLM, the engine.
• SystemPanic/vllm-windows, the upstream Windows wheel build infrastructure.
• Lorbus, the AutoRound INT4 quant of Qwen3.6-27B that makes the Ampere/Ada path fast.
• Peutlefaire, the NVFP4 quant that unlocks consumer Blackwell's full prefill throughput on the 5090.
• r/LocalLLaMA, the configs in here started from recipes posted on the subreddit, and got refined by the honest feedback in the comments.