text model · Nemotron 3 · macOS
Can I run Nemotron 3 Nano 30B-A3B on Apple M4 (24GB)?
No. Nemotron 3 Nano 30B-A3B needs ~25.1 GB even at Q4_K_M, but Apple M4 (24GB) only has ~16 GB usable.
Needs ~25.1 GB even at Q4_K_M, but only ~16 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 9.1 GB: Nemotron 3 Nano 30B-A3B needs roughly 25.1 GB at Q4_K_M and Apple M4 (24GB) leaves only about 16 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
- ~16 GB
- Device memory
- 24 GB
Which quant fits
- 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
- Memory
- 24 GB unified
- Usable for weights
- ~16 GB
- Power draw
- ~65 W
- Best runtime
- Ollama (MLX backend, preview) / MLX direct
What you can run instead
Run Nemotron 3 Nano 30B-A3B on other hardware
FAQ
Can Apple M4 (24GB) run Nemotron 3 Nano 30B-A3B?
No. Nemotron 3 Nano 30B-A3B needs ~25.1 GB even at Q4_K_M, but Apple M4 (24GB) only has ~16 GB usable.
How much memory does Nemotron 3 Nano 30B-A3B need?
Apple M4 (24GB) 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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A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/nemotron-3-nano-30b-a3b/apple-m4-24gb) 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.