text model · Granite · macOS
Can I run Granite 4.0 H Small on Apple M4 Pro (48GB)?
Yes. Granite 4.0 H Small runs on Apple M4 Pro (48GB) at Q4_K_M (~20.3 GB of ~32 GB usable).
Runs at Q4_K_M using ~20.3 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 ~11.7 GB of headroom.
- Q4_K_M needed
- ~20.3 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 granite4:32b-a9b-h llama-cli -hf ibm-granite/granite-4.0-h-small-GGUF:Q4_K_M lms get ibm-granite/granite-4.0-h-small-GGUF - Parameters
- 32B (MoE, 9B active)
- Q4_K_M size
- 18.14 GB
- Q8_0 size
- 31.91 GB
- Context
- 128k
- Ollama tag
- granite4:32b-a9b-h
- 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 Granite 4.0 H Small on other hardware
FAQ
Can Apple M4 Pro (48GB) run Granite 4.0 H Small?
Yes. Granite 4.0 H Small runs on Apple M4 Pro (48GB) at Q4_K_M (~20.3 GB of ~32 GB usable).
How much memory does Granite 4.0 H Small need?
Apple M4 Pro (48GB) has room to spare. At Q4_K_M the weights are ~18.14 GB; with KV cache and runtime overhead, budget ~20.3 GB at a 4k context. It is a Mixture-of-Experts model (32B total / 9B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run Granite 4.0 H Small 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/granite-4-h-small/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/ibm-granite/granite-4.0-h-small
- huggingface.co/ibm-granite/granite-4.0-h-small-GGUF
- 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.