Llama 3.1 on Apple M1 Pro · 16GB

✅ Runs (tight) — Llama 3.1 8B @ Q6_K
Llama 3.1 8B at Q6_K needs ~7.6 GB (weights 6.1 GB + KV 1.0 GB + overhead 512 MB @ 8K ctx) of your 8.2 GB usable (of 16.0 GB unified memory) — fits, but little headroom — close other apps or trim context. Expect ~14–25 tok/s (usable).
context @ Q6_K: runs to 32K · won't fit past that on this rig
quantneedsspeed
~ Q8_09.5 GB~1–2 tok/s (slow)
✓ Q6_K7.6 GB~14–25 tok/s (usable)
✓ Q5_K_M6.8 GB~16–25 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 (tight)
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 · Qwen2.5 · Mistral 7B · Gemma 3

Best models for a Apple M1 Pro · 16GB →

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 M1 Pro · 16GB too:

modelsharesverdict here
Qwen2.5-Coder 8kv/128hd ✅ Runs (tight)
Llama 3.2 8kv/128hd ✅ Runs comfortably
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

…and 7 more — see the Llama 3.1 page.

Check any combo yourself: open the checker →