Can I run it?
Can I run granite 4.2 8b on Apple M5 Pro?
Tight fit
Tight fit fully in unified memory on your Apple M5 Pro at BF16, 4K context, about 9.2–13 tok/s. Weights 16.4 GB plus 0.63 GB of KV cache — 17.5 GB of 18.0 GB usable.
Best quantBF16
Working context4K
Max that fits4K
Speed9.2–13 tok/s
Weights16.4 GB
KV cache0.63 GB
Total17.5 GB
Usable18.0 GB
granite 4.2 8b details · Apple M5 Pro page · How this estimate is made
Run commands
llama.cpp
llama-server -hf ibm-granite/granite-4.2-8b-GGUF:BF16 -c 4096 -ngl 40
No matching name in the Ollama library snapshot.
LM Studio
lms get ibm-granite/granite-4.2-8b-GGUF # Then load quant BF16
Assumptions
- Estimate, not a benchmark. Decode speed uses a memory-bandwidth model at 55–75% of peak.
- KV cache is FP16. Engine overhead assumes llama.cpp (0.3 GB + 1% of the weights). Ollama is closer to 0.9 GB + 3%, LM Studio closer to 1 GB + 3%.
- BF16 size is the real GGUF file size from Hugging Face.
- Unified memory uses 75% for the GPU by default, and always leaves about 5 GB for the OS.
- Speed is quoted at 4K context, not at the maximum context that still fits.
Larger models that also fit
- Can I run Qwen3.8 27B on Apple M5 Pro? — Tight fit, UD-Q4_K_XL, 9.3–13 tok/s
- Can I run gemma 4 12b it on Apple M5 Pro? — Runs, UD-Q8_K_XL, 10–14 tok/s
- Can I run Qwen3.5 9B on Apple M5 Pro? — Tight fit, BF16, 9.3–13 tok/s
Updated 2026-10-24. Profile on this page is the device default.