Qwen3.8-27B-OptiQ-4bit

mlx-community/Qwen3.8-27B-OptiQ-4bit#

Built with mlx-optiq, the MLX-native toolkit to quantize, fine-tune, and serve LLMs locally on Apple Silicon, no PyTorch and no cloud. Try the Lab · All OptiQ quants · Docs

A 4-bit mixed-precision MLX quant of Qwen/Qwen3.8-27B, an image+text model. Per-layer bit-widths come from a KL-divergence sensitivity pass on a six-domain calibration mix (prose · reasoning · code · agent · tool-call · constraint-bearing instructions). Sensitive layers go to 8-bit; robust ones stay at 4-bit.

The vision tower is kept at bf16 in a sidecar, so this repo loads text-only under stock mlx-lm and image+text under OptiQ.

Quantization details#

Qwen3.8-27B is a hybrid: three quarters of its 64 blocks use linear attention (five projections each) and the rest full attention, which is why the sweep scores 498 tensors rather than the ~450 a dense model of this size would have.

We follow the same naming convention llama.cpp uses for Q4_K_M and similar mixed-precision quants: the "4-bit" label is for the predominant precision, not the weighted average.

Usage#

Load it with mlx-lm for text:

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("mlx-community/Qwen3.8-27B-OptiQ-4bit")
response = generate(
    model, tokenizer,
    prompt="Explain quantum computing in simple terms.",
    max_tokens=200,
)

For image+text, mixed-precision KV-cache serving, sensitivity-aware LoRA fine-tuning, an OpenAI + Anthropic-compatible inference server, hot-swap mounted adapters and sandboxed Python execution, install mlx-optiq:

pip install mlx-optiq
optiq serve --model mlx-community/Qwen3.8-27B-OptiQ-4bit

See the Qwen family guide on mlx-optiq.com for sampling defaults, training recipes, and family-specific caveats.

Benchmarks#

18.9 GB on disk.

Every metric gets one equal vote, and disk size is reported next to the score as an honest second axis rather than folded into it. See the eval-framework writeup for the methodology.

Using the head outside optiq serve: optiq/mtp.safetensors holds Qwen's original MTP tensors, so its seven RMSNorm weights (mtp.*norm*) are zero-centred, applied as 1 + w. The main weights have the +1 folded in, as MLX expects. Add 1.0 to all seven norms when loading the head into another runtime.

Links#

• Project website: mlx-optiq.com
• PyPI: pypi.org/project/mlx-optiq
• Calibration mix: mlx-optiq.com/blog/calibration-mix
• Eval framework: mlx-optiq.com/blog/eval-framework
• Base model: Qwen/Qwen3.8-27B

Quantize your own#

This quant was produced by mlx-optiq. Point it at any Hugging Face model to get the same sensitivity-aware mixed precision:

pip install mlx-optiq
optiq convert Qwen/Qwen3.8-27B --target-bpw 5.0 --candidate-bits 4,8
optiq lab   # or drive the whole thing from the browser