Code Llama on Apple M3 Pro · 18GB

✅ Runs (tight) — Code Llama 7B @ Q5_K_M
Code Llama 7B at Q5_K_M needs ~9.0 GB (weights 4.5 GB + KV 4.0 GB + overhead 512 MB @ 8K ctx) of your 9.1 GB usable (of 18.0 GB unified memory) — fits, but little headroom — close other apps or trim context. Expect ~9–15 tok/s (usable).
context @ Q5_K_M: comfortable to 4K · runs to its full 16K
quantneedsspeed
~ Q8_011.2 GB~0.9–2 tok/s (slow)
✓ Q5_K_M9.0 GB~9–15 tok/s (usable)
✓ Q4_K_M8.3 GB~10–17 tok/s (usable)
$runlocal install codellama:7b
runlocal verdict card
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Code Llama: Runs (tight)
for your own README — links back here

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

Also runs on a Apple M3 Pro: DeepSeek-R1 (distill) · Qwen2.5 · Mistral 7B · Qwen3 · Llama 3.1

Best models for a Apple M3 Pro · 18GB →

Architecturally similar

These share Code Llama's KV-cache geometry — they size memory the same way, so here's how they fit on a Apple M3 Pro · 18GB 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 ✅ Runs (tight)
Codestral 8kv/128hd 🐢 CPU-only (slow)
Llama 3.2 Vision 8kv/128hd ✅ Runs (tight)
Mixtral 8x7B 8kv/128hd ❌ Won't fit

…and 8 more — see the Code Llama page.

Check any combo yourself: open the checker →