NVIDIA-Nemotron-3-Nano-4B-OptiQ-4bit
nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16, quantized by OptiQ for Apple Silicon.
On disk3.1 GB
Memoryabout 5 GB
Capability Score66.68
Capability Score breakdown
The six metrics behind this model's Capability Score of 66.68, as reported in the model card. Values are compared with uniform 4-bit.
- This model
- Uniform 4-bit
Use it
Install
pip install mlx-optiqServe it
An OpenAI- and Anthropic-compatible server on this Mac. Every app below can use it.
optiq serve --model mlx-community/NVIDIA-Nemotron-3-Nano-4B-OptiQ-4bitOptiQ Code
The terminal coding agent uses whatever model optiq serve is running.
optiq serve --model mlx-community/NVIDIA-Nemotron-3-Nano-4B-OptiQ-4bit
optiq codeOptiQ Lab
optiq lab --model mlx-community/NVIDIA-Nemotron-3-Nano-4B-OptiQ-4bitClaude Code
optiq run claude -m mlx-community/NVIDIA-Nemotron-3-Nano-4B-OptiQ-4bitCodex
optiq run codex -m mlx-community/NVIDIA-Nemotron-3-Nano-4B-OptiQ-4bitOpenCode
optiq run opencode -m mlx-community/NVIDIA-Nemotron-3-Nano-4B-OptiQ-4bitMistral Vibe
optiq run vibe -m mlx-community/NVIDIA-Nemotron-3-Nano-4B-OptiQ-4bitHermes Agent
optiq run hermes -m mlx-community/NVIDIA-Nemotron-3-Nano-4B-OptiQ-4bitOpenClaw
optiq run openclaw -m mlx-community/NVIDIA-Nemotron-3-Nano-4B-OptiQ-4bitoptiq run starts optiq serve if it is not running, sets the agent up for this session only, and stops the server when the agent exits.
mlx-lm, text only
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/NVIDIA-Nemotron-3-Nano-4B-OptiQ-4bit")
print(generate(model, tokenizer, prompt="Hello", max_tokens=256))Details
| Family | Nemotron · Nemotron docs |
|---|---|
| Architecture | Nemotron-H |
| Base model | nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 |
| Size on disk | 3.1 GB |
| Memory needed | about 5 GB |
| Images | no, text only |
| Capability Score | 66.68 (from the model card) |
| Downloads | 384 in the last month |
| Updated | 2026-09-14 |
| Hugging Face | mlx-community/NVIDIA-Nemotron-3-Nano-4B-OptiQ-4bit |