Qwen3.8-27B-W4A16-AWQ

Qwen3.8-27B — W4A16 (AWQ, g128)

<p> <img src="https://img.shields.io/badge/weights-INT4%20W4A16-2a78d6" alt="INT4 W4A16"> <img src="https://img.shields.io/badge/size-19.5%20GB-2a78d6" alt="19.5 GB"> <img src="https://img.shields.io/badge/vision-intact-1baf7a" alt="vision intact"> <img src="https://img.shields.io/badge/MTP-validated-1baf7a" alt="MTP validated"> <img src="https://img.shields.io/badge/V100%20%C3%972-serves-1baf7a" alt="2xV100"> <img src="https://img.shields.io/badge/license-Apache--2.0-52514e" alt="Apache 2.0"> </p>

INT4 weight-only quantization of Qwen/Qwen3.8-27B — the whole model, nothing stripped: vision tower intact, MTP speculative-decoding head intact, thinking mode calibrated with real reasoning traces. 55 GB BF16 → 19.5 GB, which puts a current-generation 27B VLM (with headroom for 32K+ context) on a pair of 32 GB V100s — and it serves on modern GPUs with stock vLLM, no ceremony.

Validated end-to-end on 2×V100-32GB under 1Cat-vLLM 1.2.2 (SM70 TurboMind dequant path): coherence, think-tag discipline, image understanding, math, long-form generation, and MTP speculative decoding in both draft modes.

At a glance

Measured performance

Methodology: warm serve (2 discarded warmup generations), fixed-length generations via ignore_eos so every run produces exactly the stated token count, official Qwen3.8 sampling per mode (thinking 1.0/0.95/20, instruct 0.7/0.80/20 + presence 1.5), varied prompts. Reported as mean ± sd tokens/s. Rig: 2×V100-32GB, 1Cat-vLLM 1.2.2, TP2, fp8_e5m2 KV, max_num_seqs 4, MTP K=2.

Pick the draft mode by workload. Spec-decode verification rejection-samples against the target model, so output quality is identical in both modes — only acceptance (speed) differs. At the official temp-1.0 thinking sampling, probabilistic proposes from the same distribution being verified and wins every regime (~+6%). On low-temperature workloads the two converge (see the instruct row); at temperature 0, greedy is the natural choice.

Quality validation (passed on this rig): factual coherence, think-tag discipline (zero <think> leakage with thinking disabled), vision (image understanding through the VLM path), GSM8K sample 3/3, and long-form generation with no repetition/degeneration.

The base model

Qwen3.8-27B is the compact dense model of the Qwen3.8 generation: a native vision-language model with flexible thinking control (reasoning_effort, preserve_thinking), built for long-horizon agentic work. The hidden layout is 16 blocks of 3×(GatedDeltaNet→FFN) → 1×(GatedAttention→FFN) — that 3:1 linear-attention hybrid is what makes 262K native context practical, and is also exactly what makes it non-trivial to quantize (see below).

Official base-model (BF16) benchmarks, from the Qwen3.8-27B model card — quantization applies on top of these; they are not re-measured on this checkpoint:

Why this build is careful

Thinking-mode calibration

Qwen3.8 deploys with thinking on by default — so this quant was calibrated in thinking mode. 256 samples @ 1024 tokens of real <think>…</think> reasoning traces from Magpie-Reasoning-V2-250K-CoT (filtered to complete traces), applied with enable_thinking=True.

Calibrating a thinking model with enable_thinking=False shows the quantizer an empty think block on every sample; that calibration/deployment mismatch is documented to corrupt <think>-tag behavior after quantization (llm-compressor #2680 / #2681). This build never has that mismatch — and the think-tag battery checks confirm it.

Full hybrid-attention AWQ smoothing

AWQ smoothing runs across the entire hybrid topology — all four projection groups (in_proj_qkv, in_proj_z, in_proj_b, in_proj_a) on the 48 GatedDeltaNet layers as well as the 16 full-attention layers — not just the vanilla-attention subset. Two architecture-specific details make that work:

• llmcompressor pinned to 0.12.1.dev30 — later 0.12.x has a regression that crashes replaying the linear-attention smoothing mapping on GatedDeltaNet models.
• apply_mask_to_padding_states neutralized during calibration — fx tracing can't handle its shape-dependent control flow, and calibration batches are size 1 (no padding exists to mask).

Sequential calibration over Qwen3_5DecoderLayer, moe_calibrate_all_experts irrelevant (dense).

MTP head preserved — and it works

The base model's 15-tensor MTP head is grafted back verbatim in BF16 as model-mtp.safetensors and listed in quantization_config.ignore (transformers drops mtp.* at load time, so the quantizer never sees it; high-precision MTP heads are standard practice — the head sits at the end of the network and drives speculative decoding, where draft quality directly sets acceptance rate).

Speculative decoding over the packed body is validated on SM70: 92.5% draft acceptance at K=2. There is one MTP layer; higher num_speculative_tokens reuses it with decaying acceptance — K=2 is the sweet spot on V100.

Serving

Stock vLLM (Ampere or newer)

compressed-tensors W4A16 is natively supported:

vllm serve philbert440/Qwen3.8-27B-W4A16-AWQ --max-model-len 32768 \
  --speculative-config '{"method": "mtp", "num_speculative_tokens": 2}'

2×V100 / SM70 — 1Cat-vLLM 1.2.2

Volta lacks INT4 tensor-core paths in stock vLLM; the 1Cat-vLLM fork's SM70 TurboMind dequant path serves this checkpoint at the numbers above:

VLLM_SM70_QUANT_BACKEND=turbomind VLLM_SM70_FLASH_ATTN_V100=1 \
VLLM_SM70_MTP_DYNAMIC_DRAFT_VOCAB_DEFAULT=0 NCCL_P2P_DISABLE=1 \
python -m vllm.entrypoints.openai.api_server \
  --model philbert440/Qwen3.8-27B-W4A16-AWQ \
  --dtype half --attention-backend FLASH_ATTN_V100 \
  --tensor-parallel-size 2 --gpu-memory-utilization 0.78 \
  --max-model-len 32768 --kv-cache-dtype fp8_e5m2 \
  --enable-prefix-caching --reasoning-parser qwen3 \
  --speculative-config '{"method":"mtp","num_speculative_tokens":2,"attention_backend":"FLASH_ATTN_V100","draft_sample_method":"probabilistic"}'

SM70 notes, learned the hard way:

• ninja must be on PATH — the dequant kernels JIT-compile at startup.
• VLLM_SM70_MTP_DYNAMIC_DRAFT_VOCAB_DEFAULT=0 is required for max_num_seqs > 1 with MTP.
• NCCL_P2P_DISABLE=1 on V100 pairs without NVLink — leaving P2P on costs ~4× throughput.
• Boot takes ~3–5 min (weight load + torch.compile + CUDA graph capture); poll /v1/models.

Recommended sampling (official)

Straight from the Qwen team's best practices — these apply unchanged to this quant:

• Thinking is on by default; disable per request with "chat_template_kwargs": {"enable_thinking": false}. Reasoning depth responds to reasoning_effort, and the model skips thinking on trivial prompts by design.
• presence_penalty can be raised toward 2.0 to curb repetition, at some risk of language mixing on the instruct side.
• For agentic work, give it room: Qwen recommends generous output budgets (up to 262K reasoning / 128K final within the 1M window) rather than tight max_tokens limits.

Long context — 262K native, 1M with YaRN

The checkpoint carries the base model's native 262,144-token context. For longer totals, the official YaRN override works with this quant on vLLM:

VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 vllm serve philbert440/Qwen3.8-27B-W4A16-AWQ \
  --max-model-len 1000000 \
  --hf-overrides '{"text_config": {"rope_parameters": {"mrope_interleaved": true, "mrope_section": [11, 11, 10], "rope_type": "yarn", "rope_theta": 10000000, "partial_rotary_factor": 0.25, "factor": 4.0, "original_max_position_embeddings": 262144}}}'

Per Qwen's note: static YaRN applies the scaling factor at all lengths and can slightly hurt short-text quality — enable it only when you need it, and size factor to your real context (e.g. 2.0 for ~524K). For hour-scale video work, see the base card's video_preprocessor_config.json guidance (longest_edge: 469762048).

Provenance

Quantized from the official BF16 release with llm-compressor (AWQModifier, recipe in recipe.yaml). No fine-tuning, no ablation, no chat-template edits — tokenizer, chat template, and preprocessor configs are verbatim from the base model.

Citation

Per the Qwen team, for the base model:

@misc{qwen38,
    title = {{Qwen3.8-Max}: A New Bar for Coding and Cowork},
    url = {https://qwen.ai/blog?id=qwen3.8},
    author = {{Qwen Team}},
    month = {August},
    year = {2026}
}

Changelog

• 2026-08-15 — tokenizer fix. tokenizer.json / tokenizer_config.json were re-serialized by the llm-compressor calibration run and shipped with an active truncation block (max_length 1024/2048) plus a drifted pre-tokenizer regex (and, on the Qwen3.6-based repos, 7 phantom audio/TTS special tokens the base model does not define). That broke image inputs larger than the limit under transformers 5 / vLLM (Mismatch in image token count, surfacing as an HTTP 400 Failed to apply Qwen3VLProcessor). Both files are now byte-identical to the upstream base model's (vocab/merges/added tokens were always identical — this is a metadata-only restore). If you downloaded before this date, re-fetch those two files. Thanks to @elBuffo for the report.