mlx-community/gpt-oss-20b-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. All OptiQ quants · Docs

A sensitivity-driven mixed-precision 4-bit MLX quant of OpenAI's gpt-oss-20b, produced by mlx-optiq. OptiQ measures each layer's quantization sensitivity and assigns per-layer bit-widths (4 or 8) under a target budget, instead of quantizing every layer to the same width.

gpt-oss ships natively in MXFP4 (its experts are already 4-bit). That makes a naive uniform-4-bit re-quantization unusually lossy, since it compounds error on top of weights that are already at the 4-bit floor. OptiQ keeps the layers that matter at 8-bit, so it recovers most of what uniform-4 drops.

Capability Score

Six-benchmark mean (MMLU, GSM8K, IFEval, BFCL, HumanEval, HashHop), measured against a genuine uniform-4-bit baseline built from the same weights.

Benchmark	OptiQ-4bit	uniform-4	Δ
MMLU (5-shot)	54.7	38.0	+16.7
GSM8K	83.0	74.3	+8.7
IFEval (strict)	61.9	55.3	+6.7
BFCL (simple)	70.5	59.5	+11.0
HumanEval (pass@1)	82.9	76.8	+6.1
HashHop (multi-hop retrieval)	78.0	19.0	+59.0
Capability Score	71.84	53.81	+18.03

OptiQ wins all six. The largest gap is HashHop (long-context multi-hop retrieval), where uniform-4 collapses to 19% while OptiQ holds 78%: uniform 4-bit erodes the long-range attention precision that retrieval depends on, and OptiQ's mixed precision keeps those layers at 8-bit.

What it is

Property	Value
Base	openai/gpt-oss-20b (24-layer MoE, 32 experts, 4 active per token, attention sinks)
Method	OptiQ mixed-precision (per-layer sensitivity, 4/8-bit knapsack)
Bit allocation	33 layers @ 4-bit, 88 @ 8-bit
Achieved bits-per-weight	5.03
On disk	11 GB
Resident while running	~11.6 GB

Run it

gpt-oss needs mlx-lm and import optiq (keep it for the rest of the toolkit):

pip install -U mlx-optiq "mlx-lm @ git+https://github.com/ml-explore/mlx-lm.git"

from mlx_lm import load, generate
from mlx_lm.sample_utils import make_sampler

model, tok = load("mlx-community/gpt-oss-20b-OptiQ-4bit")
prompt = tok.apply_chat_template(
    [{"role": "user", "content": "Explain mixed-precision quantization in one sentence."}],
    tokenize=False, add_generation_prompt=True, reasoning_effort="low",
)
print(generate(model, tok, prompt=prompt, max_tokens=512, sampler=make_sampler(temp=0.7)))

gpt-oss is a reasoning model using the harmony response format: it emits an analysis channel before a final channel. Pass reasoning_effort ("low", "medium", "high") to control how much it reasons, and read the answer from the final channel.

Serve it with the OptiQ Lab + OpenAI-compatible API:

optiq serve --model mlx-community/gpt-oss-20b-OptiQ-4bit

Notes

• No KV-cache quantization. gpt-oss uses attention sinks, which mlx-lm's quantized-SDPA path does not support, so this quant ships without a kv_config.json. Weight quantization (the OptiQ result above) is unaffected.
• This is a weight-only quant; it loads under stock mlx-lm as a standard gpt_oss model.
• The full method is described at mlx-optiq.com/docs.