text model · Nemotron 3 · macOS
Can I run Nemotron Cascade 2 30B-A3B on Apple M4 Pro (48GB)?
Yes. Nemotron Cascade 2 30B-A3B runs on Apple M4 Pro (48GB) at Q4_K_M (~25.1 GB of ~32 GB usable).
Runs at Q4_K_M using ~25.1 GB of ~32 GB usable.
That figure is at a 4k context and moves about ±15% as context length changes. Apple M4 Pro (48GB) leaves ~6.9 GB of headroom.
- Q4_K_M needed
- ~25.1 GB
- Usable on device
- ~32 GB
- Device memory
- 48 GB
- Best quant
- Q4_K_M
Which quant fits
Run it
Pick your tool. All 3 load the same Q4_K_M weights.
ollama run nemotron-cascade-2:30b llama-cli -hf bartowski/nvidia_Nemotron-Cascade-2-30B-A3B-GGUF:Q4_K_M lms get bartowski/nvidia_Nemotron-Cascade-2-30B-A3B-GGUF - Parameters
- 30B (MoE, 3B active)
- Q4_K_M size
- 23.03 GB
- Q8_0 size
- 31.28 GB
- Context
- 256k
- Ollama tag
- nemotron-cascade-2:30b
- Memory
- 48 GB unified
- Usable for weights
- ~32 GB
- Power draw
- ~140 W
- Best runtime
- Ollama (MLX backend) / MLX direct
You could also run
Run Nemotron Cascade 2 30B-A3B on other hardware
FAQ
Can Apple M4 Pro (48GB) run Nemotron Cascade 2 30B-A3B?
Yes. Nemotron Cascade 2 30B-A3B runs on Apple M4 Pro (48GB) at Q4_K_M (~25.1 GB of ~32 GB usable).
How much memory does Nemotron Cascade 2 30B-A3B need?
Apple M4 Pro (48GB) has room to spare. At Q4_K_M the weights are ~23.03 GB; with KV cache and runtime overhead, budget ~25.1 GB at a 4k context. It is a Mixture-of-Experts model (30B total / 3B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run Nemotron Cascade 2 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-cascade-2-30b-a3b/apple-m4-pro-48gb) Sources
- apple.com/newsroom/2024/10/apple-introduces-m4-pro-and-m4-max
- apple.com/newsroom/2024/10/new-macbook-pro-features-m4-family-of-chips-and-apple-intelligence
- blog.peddals.com
- github.com/ml-explore
- github.com/raullenchai
- huggingface.co/bartowski
- huggingface.co/nvidia
- lmstudio.ai
- ollama.com
- support.apple.com/en-us/103253
- support.apple.com/en-us/121553
- support.apple.com/en-us/121555
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.