Note: MMLU-Redux uses a 1500-sample subset; other tasks are full. Decoding/prompts/filters are lm-eval-harness defaults, so absolute scores may differ from the official Qwen3.6-27B numbers. The goal is the BF16↔INT4 delta under identical conditions, not exact replication of the baseline.

Both models evaluated under identical conditions with lm-evaluation-harness: greedy decoding (temperature=0), enable_thinking=False, seed=0. Long-CoT tasks use max_gen_toks=4096; HumanEval served via /v1/completions (raw, no chat template) so the harness's \\ndef / \\nclass stop sequences fire correctly.

Inference Performance

Single-stream measurement on the same hardware, identical request (337 input / 42 output tokens):

INT4 delivers ~2.4× higher throughput and ~2.4× lower latency at single-stream — the bandwidth savings from 4-bit weights translate almost 1:1 into decode-time speed-up (output tok/s and TPOT).

Quantization recipe

Setting	Value
Method	GPTQ
Bits	4 (weight-only)
Group size	128
`desc_act`	True (activation-order)
`damp_percent`	0.01
Symmetric	True
Calibration	C4 (`en`), 256 samples × 2048 tokens
Tool	GPTQModel v7
Effective bits / weight	4.29 BPW

The vision encoder (model.visual.*) is intentionally left in BF16 — only the language-model weights are quantized.

Usage

With GPTQModel (recommended)

python

from gptqmodel import GPTQModel
from transformers import AutoTokenizer

model_id  = "AxisQuant/Qwen3.6-27b-gptq-int4"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model     = GPTQModel.load(model_id, device_map="auto", trust_remote_code=True)

messages = [{"role": "user", "content": "Explain GPTQ in one sentence."}]
text   = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True, enable_thinking=False,
)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
out    = model.generate(**inputs, max_new_tokens=256, do_sample=False)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

With transformers

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id  = "AxisQuant/Qwen3.6-27b-gptq-int4"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model     = AutoModelForCausalLM.from_pretrained(
    model_id, device_map="auto", trust_remote_code=True,
)

Hardware

Weights: 18 GB on disk · ~14 GB VRAM at load
Single-GPU friendly: comfortably fits on a 24 GB consumer card (RTX 3090 / 4090) for short-to-mid context
Long context (64K+ tokens): H100 80 GB or A100 80 GB recommended

Limitations

Only the language-model weights are quantized; the vision encoder remains in BF16
Calibration set was English C4 — heavy non-English or domain-specific workloads may benefit from re-quantizing on a matching corpus
Thinking mode (enable_thinking=True) works but is significantly slower — enable only when reasoning quality matters more than latency

License

Inherits the license of the base model. See the Qwen/Qwen3.6-27B model page for terms.

Citation

Base model

bibtex

@misc{qwen3.6-27b,
    title  = {{Qwen3.6-27B}: Flagship-Level Coding in a {27B} Dense Model},
    author = {{Qwen Team}},
    month  = {April},
    year   = {2026},
    url    = {https://qwen.ai/blog?id=qwen3.6-27b}
}

Quantization method

bibtex

@article{frantar2022gptq,
    title   = {{GPTQ}: Accurate Post-training Compression for Generative Pretrained Transformers},
    author  = {Frantar, Elias and Ashkboos, Saleh and Hoefler, Torsten and Alistarh, Dan},
    journal = {arXiv preprint arXiv:2210.17323},
    year    = {2022}
}