mlx-community/NVIDIA-Nemotron-3-Nano-4B-OptiQ-4bit

mlx-community/NVIDIA-Nemotron-3-Nano-4B-OptiQ-4bit

Built with [mlx-optiq](https://mlx-optiq.com), 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 mlx-community/NVIDIA-Nemotron-3-Nano-4B-BF16 produced by mlx-optiq, the sensitivity-aware quantization toolkit for Apple Silicon. +1.6 pp on GSM8K over stock uniform 4-bit at the same per-weight precision target.

Nemotron 3 Nano is a hybrid Mamba2 + attention model. Of the 42 backbone blocks, 4 are full attention and the rest are Mamba2 SSM or MLP. OptiQ measures each linear's KL-divergence sensitivity against a bf16 reference forward pass and assigns 4-bit or 8-bit per-layer based on that signal. Sensitive layers go to 8-bit; robust ones stay at 4-bit.

Quantization details

We follow the same naming convention llama.cpp uses for Q4KM-style mixed-precision quants: the "4-bit" label is for the predominant precision, not the weighted average.

Usage

Load it with mlx-lm (the custom NemotronH modeling files ship in the repo and are picked up automatically):

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("mlx-community/NVIDIA-Nemotron-3-Nano-4B-OptiQ-4bit")
response = generate(
    model, tokenizer,
    prompt="Explain why hybrid Mamba+attention models scale to long contexts.",
    max_tokens=300,
)

For mixed-precision KV-cache serving and sensitivity-aware LoRA fine-tuning, install `mlx-optiq`:

pip install mlx-optiq

# Serve with the bundled KV-cache recipe
optiq serve --model mlx-community/NVIDIA-Nemotron-3-Nano-4B-OptiQ-4bit \
            --kv-config kv_config.json

Benchmarks

Six-metric Capability Score (mean of MMLU + GSM8K + IFEval + BFCL + HumanEval + HashHop). Apples-to-apples comparison against stock uniform 4-bit:

Every metric gets one equal vote. Disk size is reported next to the score as an honest second axis instead of being folded into the score. On this Mamba-attention hybrid the mixed allocation runs a little richer than on a pure transformer, Mamba2 SSM blocks carry only two linears each, so the sensitivity probe upgrades more of them to 8-bit, which is why the disk delta is larger than on the Qwen / Gemma families. See the eval-framework writeup for the full methodology.

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: mlx-community/NVIDIA-Nemotron-3-Nano-4B-BF16

Base model

This is a quantized derivative of NVIDIA Nemotron 3 Nano 4B. See the NVIDIA Nemotron Open Model License for terms, the quant is distributed under the same license as the base.

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 <hf-model-id> --target-bpw 5.0 --candidate-bits 4,8
optiq lab   # full local workbench: chat, compare, quantize, fine-tune