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Apple M2 Pro

16GB unified memory ·  Apple Silicon (Metal/MLX)

Quick answer: The fastest listed local LLM on Apple M2 Pro is llama3.2-3b at 82 tok/s decode, with 9 Q4_K_M models marked as fitting this hardware.
⚠️ Apple Silicon uses unified memory shared between CPU and GPU. macOS typically reserves 3–4GB for system use — effective available memory is ~12GB, not the full 16GB spec. Models near the top of the VRAM limit may OOM on a loaded system.
ℹ️ VRAM estimates assume GGUF format and 2048-token context. Running at 8k+ context adds 2–4GB. Tokens/sec = generation (decode) speed only.7 rows currently link to community benchmark sources.
ModelQuantVRAMTok/sDataFits
llama3.2-3bQ4_K_M2GB82Community benchmark✓
gemma3-4bQ4_K_M3GB75Community benchmark✓
mistral-7bQ4_K_M4.7GB52Community benchmark✓
qwen2.5-7bQ4_K_M4.7GB50Community benchmark✓
llama3.1-8bQ4_K_M5GB48Community benchmark✓
deepseek-r1-7bQ4_K_M4.7GB46estimate✓
gemma3-12bQ4_K_M7.5GB30Community benchmark✓
qwen2.5-14bQ4_K_M9GB22Community benchmark✓
phi-4-14bQ4_K_M9GB20estimate✓
gemma3-27bQ4_K_M16.5GB—estimate✗ No
llama3.1-70bQ4_K_M42GB—estimate✗ No
mistral-24bQ4_K_M14.4GB—estimate✗ No
nemotron-51bQ4_K_M30.6GB—estimate✗ No
qwen2.5-32bQ4_K_M19.5GB—estimate✗ No
qwen2.5-72bQ4_K_M43.5GB—estimate✗ No
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Apple M2 Pro local LLM FAQ

What LLMs can run on Apple M2 Pro?

9 Q4_K_M models in this dataset are marked as fitting on Apple M2 Pro. Start with the table rows marked as fitting, then compare VRAM and decode tok/s.

What is the fastest local LLM on Apple M2 Pro?

llama3.2-3b is the fastest listed model on Apple M2 Pro at 82 decode tokens per second.

How much VRAM does Apple M2 Pro have for local LLMs?

Apple M2 Pro has 16GB unified memory, but macOS can reserve several GB, so the practical model budget is closer to 12GB on a loaded system.

Can Apple M2 Pro run 70B local LLMs?

Apple M2 Pro is not marked as fitting the listed 70B-class Q4_K_M models in this dataset. Check the fit column before trying larger context windows.

What quantization should I use on Apple M2 Pro?

Use Q4_K_M as the baseline for this site. It is the comparison format used across the current benchmark table and keeps VRAM requirements predictable.

Last updated: 2026-06-11 ·Source ·Improve this data