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philbert440/
Qwen3.8-27B-W4A16-AWQ

Name: Qwen3.8-27B-W4A16-AWQ
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Qwen3.8-27B — W4A16 (AWQ, g128)

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


Base model	Qwen/Qwen3.8-27B — dense 27B vision-language model, 64 layers (16 full-attention + 48 GatedDeltaNet linear-attention), thinking on by default
Format	`compressed-tensors` pack-quantized · INT4 · group 128 · asymmetric · weight-only
Scale search	MSE observer (reconstruction-error minimization, not min-max)
Files	`model.safetensors` 18.7 GB (packed body) + `model-mtp.safetensors` 0.85 GB (BF16 MTP head)
Quantized	400 linears — all attention + MLP projections, including 144 GatedDeltaNet projections
Kept in BF16	vision tower (333 tensors) · `linear_attn.in_proj_a/b` · `embed_tokens` · `lm_head` · MTP head
Context length	262,144 native · extensible to 1M with YaRN (see below)
Reference hardware	2×V100-32GB (TP2) via 1Cat-vLLM 1.2.2 · any Ampere+ GPU via stock vLLM

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.

Regime	greedy draft	probabilistic draft
512-tok, thinking (n=10)	55.6 ± 1.7	59.3 ± 1.4
2048-tok, thinking (n=3)	53.2 ± 1.8	56.3 ± 1.1
512-tok, instruct (n=6)	54.7 ± 1.9	55.5 ± 1.5
4-way concurrent 512-tok, aggregate (n=3)	161.9 ± 20.6	164.8 ± 23.4
Mean acceptance length, whole workload	2.34	2.52

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:

Benchmark	Qwen3.8-27B	Qwen3.6-27B
Terminal Bench 2.1 (Terminus)	73.0	63.4
SWE-bench Pro	53.5	57.6
NL2Repo-Bench	42.3	36.2
GPQA Diamond	89.2	87.8
HLE	30.8	24.0
LiveCodeBench v6	83.9	89.6
IFBench	69.1	79.1
OmniDocBench 1.5 (VL)	91.1	89.4
RealWorldQA (VL)	85.9	84.1
ERQA (VL)	65.5	62.5
OSWorld-Verified (VL)	63.9	73.3

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:

bash

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:

bash

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:

Mode	temperature	top_p	top_k	min_p	presence_penalty
Thinking (default)	1.0	0.95	20	0.0	0.0
Instruct (non-thinking)	0.7	0.80	20	0.0	1.5

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:

bash

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:

bibtex

@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.