Can I run it?

Can I run gemma 4 12b it on Apple M5 Max (40-core GPU)?

Runs

Runs fully in unified memory on your Apple M5 Max (40-core GPU) at BF16, 8K context, about 12–17 tok/s. Weights 22.2 GB plus 0.81 GB of KV cache — 23.5 GB of 36.0 GB usable. Up to 200K context still fits.

gemma 4 12b it details · Apple M5 Max (40-core GPU) page · How this estimate is made

Run commands

llama-server -hf unsloth/gemma-4-12b-it-GGUF:BF16 -c 8192 -ngl 48

ollama run gemma

lms get unsloth/gemma-4-12b-it-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 8K context, not at the maximum context that still fits.

Larger models that also fit

• Can I run Qwen3.8 27B on Apple M5 Max (40-core GPU)? — Runs, UD-Q8_K_L, 12–16 tok/s
• Can I run Qwen3.6 35B A3B on Apple M5 Max (40-core GPU)? — Tight fit, Q8_0, 100–137 tok/s
• Can I run Qwen3.8 35B A3B Distill on Apple M5 Max (40-core GPU)? — Runs, Q6_K, 128–175 tok/s

Updated 2026-10-24. Profile on this page is the device default.