Llama 3.2 on Apple M1 Pro · 16GB

✅ Runs comfortably — Llama 3.2 3B @ Q8_0
Llama 3.2 3B at Q8_0 needs ~4.6 GB (weights 3.2 GB + KV 896 MB + overhead 512 MB @ 8K ctx) of your 8.2 GB usable (of 16.0 GB unified memory) — plenty of headroom. Expect ~25–45 tok/s (fast).
context @ Q8_0: comfortable to 16K · runs to 64K · won't fit past that on this rig
quantneedsspeed
✓ Q8_04.6 GB~25–45 tok/s (fast)
✓ Q5_K_M3.5 GB~35–60 tok/s (fast)
✓ Q4_K_M3.3 GB~40–65 tok/s (very fast)
$runlocal install llama3.2:3b
runlocal verdict card
download card share on x share on reddit drop it in your model-drop post
Llama 3.2: Runs comfortably
for your own README — links back here

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

Also runs on a Apple M1 Pro: DeepSeek-R1 (distill) · Qwen3 · Llama 3.1 · Qwen2.5 · Mistral 7B

Best models for a Apple M1 Pro · 16GB →

Architecturally similar

These share Llama 3.2's KV-cache geometry — they size memory the same way, so here's how they fit on a Apple M1 Pro · 16GB too:

modelsharesverdict here
Qwen2.5-Coder 8kv/128hd ✅ Runs (tight)
Llama 3.3 8kv/128hd ❌ Won't fit
Mistral Small 3 8kv/128hd 🐢 CPU-only (slow)
Mistral Nemo 8kv/128hd 🐢 CPU-only (slow)
Codestral 8kv/128hd 🐢 CPU-only (slow)
Llama 3.2 Vision 8kv/128hd ✅ Runs (tight)
Mixtral 8x7B 8kv/128hd ❌ Won't fit
Phi-4-mini 8kv/128hd ✅ Runs comfortably

…and 6 more — see the Llama 3.2 page.

Check any combo yourself: open the checker →