Llama 3.1 on Apple M2 Pro · 32GB

✅ Runs comfortably — Llama 3.1 8B @ Q8_0
Llama 3.1 8B at Q8_0 needs ~9.5 GB (weights 8.0 GB + KV 1.0 GB + overhead 512 MB @ 8K ctx) of your 18.4 GB usable (of 32.0 GB unified memory) — plenty of headroom. Expect ~12–20 tok/s (usable).
context @ Q8_0: comfortable to 32K · runs to its full 128K
quantneedsspeed
✓ Q8_09.5 GB~12–20 tok/s (usable)
✓ Q6_K7.6 GB~15–25 tok/s (usable)
✓ Q5_K_M6.8 GB~17–30 tok/s (fast)
✓ Q4_K_M6.1 GB~19–30 tok/s (fast)
$runlocal install llama3.1:8b
runlocal verdict card
download card share on x share on reddit drop it in your model-drop post
Llama 3.1: Runs comfortably
for your own README — links back here

Same model, other machines: Apple M1 · Apple M1 Pro · Apple M2 · Apple M3 Pro · Apple M4 Pro · Apple M3 Max

Also runs on a Apple M2 Pro: DeepSeek-R1 (distill) · Mistral Small 3 · Qwen3 · Qwen2.5 · Gemma 4

Best models for a Apple M2 Pro · 32GB →

Architecturally similar

These share Llama 3.1's KV-cache geometry — they size memory the same way, so here's how they fit on a Apple M2 Pro · 32GB too:

modelsharesverdict here
Qwen2.5-Coder 8kv/128hd ✅ Runs (tight)
Llama 3.2 8kv/128hd ✅ Runs comfortably
Llama 3.3 8kv/128hd 🐢 CPU-only (slow)
Mistral 7B 8kv/128hd ✅ Runs comfortably
Mistral Nemo 8kv/128hd ✅ Runs comfortably
Codestral 8kv/128hd ✅ Runs (tight)
Llama 3.2 Vision 8kv/128hd ✅ Runs comfortably
Mixtral 8x7B 8kv/128hd 🐢 CPU-only (slow)

…and 7 more — see the Llama 3.1 page.

Check any combo yourself: open the checker →