text model · Nemotron · macOS

Can I run Nemotron Nano 9B v2 on Apple M1 (8GB)?

Compatibility verdict VRAM threshold engine

No, not enough memory would not load

No. Nemotron Nano 9B v2 needs ~7.6 GB even at Q4_K_M, but Apple M1 (8GB) only has ~5.5 GB usable.

Needs ~7.6 GB Device usable ~5.5 GB

Needs ~7.6 GB even at Q4_K_M, but only ~5.5 GB is usable.

That figure is at a 4k context and moves about ±15% as context length changes.

The gap is about 2.1 GB: Nemotron Nano 9B v2 needs roughly 7.6 GB at Q4_K_M and Apple M1 (8GB) leaves only about 5.5 GB usable for a model. The lightest tracked hardware that runs Nemotron Nano 9B v2 is the Nvidia GeForce RTX 3060 (12GB) at 12 GB. See Nemotron Nano 9B v2 on Nvidia GeForce RTX 3060 (12GB).

Q4_K_M needed: ~7.6 GB
Usable on device: ~5.5 GB
Device memory: 8 GB

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Which quant fits

Quant ladder vs ~5.5 GB usable

Q2_K

~5.3 GB

Q3_K_M

~5.9 GB

Q4_K_M

~7.6 GB

Q5_K_M

~7.9 GB

Q6_K

~8.9 GB

Q8_0

~10.3 GB

FP16

~19.3 GB

The line marks Apple M1 (8GB)'s ~5.5 GB budget; rungs past it are too large.

How to run it

On macOS use LM Studio (Polished GUI, ships MLX on Apple Silicon, one-click model downloads.).

Model Nemotron

Parameters: 9B
Q4_K_M size: 6.08 GB
Q8_0 size: 8.81 GB
Context: 128k

Full Nemotron Nano 9B v2 requirements →

Device macOS

Memory: 8 GB unified
Usable for weights: ~5.5 GB
Power draw: ~39 W
Best runtime: Ollama (llama.cpp Metal backend)

Best models for Apple M1 (8GB) →

What you can run instead

Run Nemotron Nano 9B v2 on other hardware

Nvidia GeForce RTX 3060 (12GB)Nvidia GeForce RTX 4070 (12GB)Samsung Galaxy S24 Ultra Generic Android Phone (12GB RAM)iPhone 17 Pro iPhone Air

FAQ

Can Apple M1 (8GB) run Nemotron Nano 9B v2?

No. Nemotron Nano 9B v2 needs ~7.6 GB even at Q4_K_M, but Apple M1 (8GB) only has ~5.5 GB usable.

How much memory does Nemotron Nano 9B v2 need?

Apple M1 (8GB) does not have enough memory. At Q4_K_M the weights are ~6.08 GB; with KV cache and runtime overhead, budget ~7.6 GB at a 4k context.

What is the best tool to run Nemotron Nano 9B v2 on macOS?

LM Studio for a simple setup; mlx-lm for the most speed. vLLM is NOT a Mac tool, it is a CUDA/Linux serving engine. Unified memory is not a fixed VRAM slice; ~70% is usable for weights.

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Markdown

[![Nemotron Nano 9B v2 on Apple M1 (8GB)](https://localmodel.run/badge/nemotron-nano-9b/apple-m1-8gb.svg)](https://localmodel.run/can-i-run/nemotron-nano-9b/apple-m1-8gb)

Sources

Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.