Memory at each precision
| Version | Weights | With context | Smallest Mac |
|---|---|---|---|
| 8-bit | 31.9 GB | 34.5 GB | 48 GB |
| 6-bit | 24.4 GB | 26.4 GB | 36 GB |
| 4-bit | 16.9 GB | 18.4 GB | 32 GB |
| 3-bit lower quality | 13.1 GB | 14.6 GB | 24 GB |
| 2-bit lower quality | 9.4 GB | 10.9 GB | 16 GB |
On every Apple chip
The smallest memory size of each chip that runs Nemotron 3 Nano 30B A3B, the best version that fits it, and the writing speed you can expect.
| Chip | Needs | Version | Tokens/s |
|---|---|---|---|
| M6 | 32 GB | 4-bit Runs well | 38–68 |
| M5 Ultra | 96 GB | 8-bit Runs well | 146–259 |
| M5 Max | 36 GB | 6-bit Runs well | 74–131 |
| M5 Pro | 48 GB | 8-bit Runs well | 37–66 |
| M5 | 32 GB | 4-bit Runs well | 34–61 |
| M4 Max | 36 GB | 6-bit Runs well | 66–117 |
| M4 Pro | 48 GB | 8-bit Runs well | 33–59 |
| M4 | 32 GB | 4-bit Runs well | 27–48 |
| M3 Ultra | 96 GB | 8-bit Runs well | 100–177 |
| M3 Max | 36 GB | 6-bit Runs well | 48–86 |
| M3 Pro | 36 GB | 6-bit Runs well | 24–43 |
| M3 | 16 GB | 2-bit Runs well | 41–73 |
| M2 Ultra | 64 GB | 8-bit Runs well | 97–173 |
| M2 Max | 32 GB | 4-bit Runs well | 90–160 |
| M2 Pro | 32 GB | 4-bit Runs well | 45–80 |
| M2 | 16 GB | 2-bit Runs well | 41–73 |
| M1 Ultra | 64 GB | 8-bit Runs well | 97–173 |
| M1 Max | 32 GB | 4-bit Runs well | 90–160 |
| M1 Pro | 32 GB | 4-bit Runs well | 45–80 |
| M1 | 16 GB | 2-bit Runs well | 28–49 |
How to run it
- The easy way: download MLX Harness, free and open source. Search for Nemotron 3 Nano 30B A3B, download the version that fits your Mac, and chat. It also serves the model to your other apps over an OpenAI-compatible API.Download MLX HarnessFree · Apple Silicon · macOS 14.5 or later
- In Terminal with Apple’s MLX:
pip install mlx-lmmlx_lm.chat --model mlx-community/NVIDIA-Nemotron-3-Nano-30B-A3B-4bitBuild: mlx-community/NVIDIA-Nemotron-3-Nano-30B-A3B-4bit on Hugging Face. Other precisions sit next to it.
Step by step: download and run LLMs on a Mac · how to use MLX Harness · MLX vs Ollama vs llama.cpp
About Nemotron 3 Nano 30B A3B
| Made by | NVIDIA |
|---|---|
| Parameters | 30B total · 3.5B active (mixture of experts) |
| Context | 256K tokens |
| Licence | NVIDIA Nemotron Open Model License |
| Released | 2025-12 |
| Input | Text |
| Intelligence | 8.9 on the Artificial Analysis Intelligence Index |
| Weights | nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 |
Questions
- How much memory does Nemotron 3 Nano 30B A3B need?
- About 16.9 GB of weights at 4-bit, plus room for the context: 18.4 GB in all. On a Mac that means 32 GB of unified memory or more, since macOS lets the GPU use about 75% of it by default.
- How fast is Nemotron 3 Nano 30B A3B on a Mac?
- Writing speed depends on memory bandwidth. The fastest Mac for it here is an M5 Ultra with 96 GB at about 146–259 tokens a second (8-bit). The smallest Mac that runs it, with 32 GB, manages about 90–160 on an M2 Max.
- Why does Nemotron 3 Nano 30B A3B run faster than its size suggests?
- It is a mixture-of-experts model: it holds 30B parameters in memory but reads only 3.5B for each token, so it writes at the speed of a much smaller model.
Similar models
For your model card or README
Shows the smallest Mac that runs Nemotron 3 Nano 30B A3B well and links here.
[](https://mlx.app/run/nemotron-3-nano-30b-a3b)How it’s worked out
Memory: the model’s parameters at 8, 6, 4, 3 or 2 bits per weight plus MLX’s group scales (half a bit per weight), or its native format, with room for the context on top. By default macOS lets the GPU use about 75% of unified memory. Speed: memory bandwidth divided by the bytes read per token is the ceiling for one conversation; in llama.cpp’s public Apple Silicon benchmarks real runs reach about 45% to 80% of it, which is the range shown. These are estimates, not measurements: measure your own Mac on MLX Bench. Model figures from each official model card, chip figures from Apple’s Tech Specs. Checked 2026-10-19. Same data as mlx.app/compare.