Codestral on Apple M1 Pro · 16GB

🐢 CPU-only (slow) — Codestral 22B @ Q3_K_M
Codestral 22B at Q3_K_M needs ~12.4 GB (weights 10.1 GB + KV 1.8 GB + overhead 518 MB @ 8K ctx) of your 8.2 GB usable (of 16.0 GB unified memory) — runs on CPU only. Expect ~1–2 tok/s (slow).
context @ Q3_K_M: runs to 16K · won't fit past that on this rig
quantneedsspeed
✗ Q8_024.9 GB—
✗ Q5_K_M17.2 GB—
✗ Q4_K_M14.9 GB—
~ Q3_K_M12.4 GB~1–2 tok/s (slow)
$runlocal install codestral:22b
runlocal verdict card
download card share on x share on reddit drop it in your model-drop post
Codestral: CPU-only (slow)
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 Codestral'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)
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 Codestral page.

Check any combo yourself: open the checker →