Qwen3.8-27B-GPTQ-Int4

Qwen3.8-27B-GPTQ-Int4

4-bit (W4A16) GPTQ quantization of Qwen/Qwen3.8-27B, produced with LLM Compressor. The weight footprint drops to roughly a third of the BF16 release; architecture, tokenizer, chat template and processor configuration are unchanged.

[!Important] Unofficial community quantization. Not produced, reviewed or endorsed by the Qwen team or Alibaba Cloud. For the original model and its capabilities, see the Qwen3.8-27B model card.

Evaluation

Paired comparisons against the unquantized base model: same prompts, same settings, greedy decoding. Higher is better.

What is quantized

400 linear modules carry 4-bit weights: mlp.{gate,up,down}_proj across all 64 language layers, self_attn.{q,k,v,o}_proj across the 16 full-attention layers, and linear_attn.{in_proj_qkv,in_proj_z,out_proj} across the 48 linear-attention layers.

Left at BF16:

• The vision tower (model.visual.*). A structural constraint, not a choice: visual.blocks.*.mlp.linear_fc2 has 4304 input columns and group quantization needs columns % group_size == 0. 4304 = 16 x 269 with 269 prime, so no practical group size divides it. Any 4-bit quantization of this architecture leaves the vision tower unquantized.
• lm_head and the embedding table. The output projection costs accuracy out of proportion to its size; the embedding table is not a linear operator.
• linear_attn.in_proj_a and in_proj_b, the gated-delta-net gate and decay projections. Tiny, and they govern the numerically delicate state-space recurrence. Qwen's own FP8 release excludes them too.
• The mtp multi-token-prediction head, unused outside speculative decoding.

Quantization details

Deterministic given the base checkpoint and the recipe in recipe.yaml. This architecture (model_type: qwen3_5) requires a recent transformers.

Serving with vLLM

vllm serve ionos/Qwen3.8-27B-GPTQ-Int4

Sizing flags depend on your hardware and workload and are deliberately not prescribed.

On compute capability 8.9 (Ada) this is served by vLLM's MarlinLinearKernel. Marlin is mixed-precision: it unpacks the 4-bit weights to 16 bits in registers and runs the GEMM on FP16/BF16 tensor cores. No INT4 tensor-core instruction is issued on any architecture, so the gain is weight-memory bandwidth, not 4-bit arithmetic. The benefit is largest at low concurrency and narrows as batches grow and compute dominates. Benchmark your own workload shape before assuming 4-bit beats 8-bit.

[!Important] You may need to lower --max-num-seqs. Qwen3.8-27B is hybrid: three gated-delta-net layers for every full-attention layer. Every decode sequence needs a recurrent-state ("Mamba") cache block in addition to its KV cache pages, from the same memory budget. If max_num_seqs exceeds the blocks that fit, the engine refuses to start:

ValueError: max_num_seqs (256) exceeds available Mamba cache blocks (<available>).

Set --max-num-seqs at or below the number it reports, or give the cache more room. This ceiling comes from the architecture and memory budget, not from quantization.

Thinking is on by default

reasoning_effort defaults to xhigh, so the model may emit several thousand reasoning tokens before its answer. Allocate output length accordingly, or reduce it per request:

from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")

# levels: xhigh (default), medium, low
resp = client.chat.completions.create(
    model="ionos/Qwen3.8-27B-GPTQ-Int4",
    messages=[{"role": "user", "content": "..."}],
    reasoning_effort="low", max_tokens=1024,
)

# or disable entirely
resp = client.chat.completions.create(
    model="ionos/Qwen3.8-27B-GPTQ-Int4",
    messages=[{"role": "user", "content": "..."}],
    extra_body={"chat_template_kwargs": {"enable_thinking": False}}, max_tokens=1024,
)

Sampling defaults, image input and the 262,144-token context (including YaRN guidance beyond it) follow the base model card unchanged. The vision tower is unquantized, so multimodal behaviour should track the base model more closely than the language path.

Modifications to the original model

As Apache 2.0 Section 4(b) requires, the changes to the original work are:

• The linear operators listed above were converted from BF16 to 4-bit integers with group-128 symmetric scales using GPTQ, and saved in the compressed-tensors pack-quantized format. Activations are not quantized: this is W4A16, so weights are dequantized to 16-bit at inference and the matmul runs in 16-bit.
• config.json gained a quantization_config block.
• A recipe.yaml recording the LLM Compressor recipe was added.
• No other file was altered.

License

Derivative work of Qwen/Qwen3.8-27B, released under the same licence, the Apache License 2.0 (LICENSE).

Apache 2.0 requires no specific attribution phrase and the base model ships no NOTICE file. "Qwen" is used only to identify the original model; no trademark rights are granted (Section 6) and nothing here implies endorsement.

Citation

@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}
}