Qwen3.8-27B-int4-AutoRound

4-bit (AutoRound) quantization of Qwen/Qwen3.8-27B — the full Omni model in ~18 GB, so it runs on a single 24 GB consumer GPU. No RTX 6000 Pro required.

This is a weight-only INT4 quant built with Intel AutoRound, method-matched to the excellent Lorbus/Qwen3.6-27B-int4-AutoRound so you can drop 3.8 in as a direct upgrade. Packed as auto_round:auto_gptq and served with vLLM's auto_round quantization path.

Why this exists

The base model is a great local workhorse, but bf16 needs 54 GB — two big cards minimum. At INT4 the weights are **17.5 GB**, which means:

Setup	Fits?	Notes
1× RTX 3090 / 4090 (24 GB)	✅	Weights + a healthy context window
2× 24 GB (TP=2)	✅	Full 262 K context, fp8 KV cache
CPU / small VRAM	via GGUF instead	this repo targets GPU + vLLM

Built on a home server ("zuse") on a single RTX 3090 — the point being that if a 3090 can quantize it, a 3090 can run it.

Quantization recipe

• AutoRound, bits=4, group_size=128, symmetric, data_type=int
• Export format: auto_round:auto_gptq
• Calibration: NeelNanda/pile-10k, nsamples=128, iters=200, seqlen=2048
• Only the 64 language-model decoder blocks are quantized. The vision/audio towers stay in fp16.
• The tiny linear_attn.in_proj_a / in_proj_b projections (shape 48×5120, not divisible by the group size) are kept at fp16 — same choice as the 3.6 recipe, dictated by the shapes rather than taste.
• lm_head and embeddings stay fp16.

Note: Qwen3.8-27B reports architecture qwen3_5 (Qwen3_5ForConditionalGeneration) and is multimodal. AutoRound was run in text-only calibration mode (the model's text decoder is the only part quantized).

Run it with vLLM

vllm serve MKRWW/Qwen3.8-27B-int4-AutoRound \
  --served-model-name qwen3.8-27b \
  --quantization auto_round \
  --max-model-len 32768 \
  --kv-cache-dtype fp8 \
  --gpu-memory-utilization 0.92 \
  --reasoning-parser qwen3 \
  --enable-auto-tool-choice --tool-call-parser qwen3_coder \
  --trust-remote-code

For two 24 GB cards and long context, add --tensor-parallel-size 2 --max-model-len 262144.

Reasoning / serving notes

Hard-won notes from putting this into production on vLLM — they'll save you a night:

• It reasons by default on the chat endpoint. The model thinks before answering, proportional to task difficulty (more tokens on harder problems), then returns a clean answer. The reasoning improves quality — it's on, not off.
• reasoning_content is not separated by vLLM's built-in qwen3 / deepseek_r1 reasoning parsers (verified on vLLM 0.20.1 and 0.23.0). The chat template prefills <think>, so the model's output is reasoning</think>answer with no opening tag; the parsers strip the reasoning out of content but don't expose it. If you want a visible/streamed reasoning field (e.g. an Open-WebUI think-box), add a small custom --reasoning-parser-plugin that splits on the first </think>.
• Give it token headroom. Because it thinks, a tight max_tokens can be consumed by the reasoning before the answer arrives, yielding empty/truncated content. Use a generous max_tokens, or pass chat_template_kwargs={"enable_thinking": false} for short, no-think calls.

Benchmarks

Measured on the build hardware (RTX 3090) against a method-matched Qwen3.6-27B-int4-AutoRound build, identical serving config. This is a small internal sanity set, not a formal leaderboard — but it's executable and reproducible.

Quality (this build vs. the 3.6 int4 build):

Test	3.6-int4	3.8-int4
Coding — 13 tasks, run against unit tests (no-think)	12/13	13/13
Math — 12 word problems, verified	8/12	12/12
Tool-calling (valid JSON args)	✅	✅
Vision / Omni (image described)	✅	✅

Throughput (vLLM, TP=2, 1024-in/256-out, ignore_eos) — 3.8 is within noise of 3.6:

Concurrency	Output tok/s	Total tok/s
1	56	281
8	200	999
32	271	1354
64	281	1404

Single-stream ~56 tok/s; the box is prefill-bound, so long prompts cost latency, not decode speed.

Single-card (24 GB) footprint: weights 17.45 GB → with --enforce-eager leaves room for ~16 K context (≈59 K KV tokens, fp8). For the full 262 K context use two cards (TP=2).

Credits & license

• Base model: Qwen/Qwen3.8-27B (Apache-2.0) — all credit to the Qwen team.
• Quantization: Intel AutoRound.
• Recipe mirrors Lorbus/Qwen3.6-27B-int4-AutoRound.
• This derivative is released under Apache-2.0, inheriting the base model's license.