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text model · Nemotron 3 · macOS

Can I run Nemotron 3 Nano 30B-A3B on Apple M1 (8GB)?

Compatibility verdict VRAM threshold engine
No, not enough memory would not load

No. Nemotron 3 Nano 30B-A3B needs ~25.1 GB even at Q4_K_M, but Apple M1 (8GB) only has ~5.5 GB usable.

Needs ~25.1 GB Device usable ~5.5 GB

Needs ~25.1 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 19.6 GB: Nemotron 3 Nano 30B-A3B needs roughly 25.1 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 3 Nano 30B-A3B is the Nvidia GeForce RTX 5090 (32GB) at 32 GB. See Nemotron 3 Nano 30B-A3B on Nvidia GeForce RTX 5090 (32GB).

Q4_K_M needed
~25.1 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
~14.7 GB
Q3_K_M
~16.8 GB
Q4_K_M
~25.1 GB
Q5_K_M
~23.5 GB
Q6_K
~26.7 GB
Q8_0
~33.4 GB
FP16
~60.9 GB
The line marks Apple M1 (8GB)'s ~5.5 GB budget; rungs past it are too large.
Model Nemotron 3
Parameters
30B (MoE, 3.5B active)
Q4_K_M size
22.96 GB
Q8_0 size
31.28 GB
Context
256k
Ollama tag
nemotron-3-nano:30b-a3b
Full Nemotron 3 Nano 30B-A3B 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 3 Nano 30B-A3B on other hardware

FAQ

Can Apple M1 (8GB) run Nemotron 3 Nano 30B-A3B?

No. Nemotron 3 Nano 30B-A3B needs ~25.1 GB even at Q4_K_M, but Apple M1 (8GB) only has ~5.5 GB usable.

How much memory does Nemotron 3 Nano 30B-A3B need?

Apple M1 (8GB) does not have enough memory. At Q4_K_M the weights are ~22.96 GB; with KV cache and runtime overhead, budget ~25.1 GB at a 4k context. It is a Mixture-of-Experts model (30B total / 3.5B active), so all experts must stay in memory; memory tracks total params, not active params.

What is the best tool to run Nemotron 3 Nano 30B-A3B 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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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.