Code Llama on Apple M4 Pro · 48GB

✅ Runs comfortably — Code Llama 34B @ Q5_K_M
Code Llama 34B at Q5_K_M needs ~25.0 GB (weights 22.4 GB + KV 1.5 GB + overhead 1.1 GB @ 8K ctx) of your 33.0 GB usable (of 48.0 GB unified memory) — plenty of headroom. Expect ~6–9 tok/s (slow).
context @ Q5_K_M: comfortable to 8K · runs to its full 16K
quantneedsspeed
~ Q8_036.6 GB~0.5–0.8 tok/s (slow)
✓ Q5_K_M25.0 GB~6–9 tok/s (slow)
✓ Q4_K_M21.5 GB~7–11 tok/s (usable)
✓ Q3_K_M17.6 GB~8–13 tok/s (usable)
$runlocal install codellama:34b
runlocal verdict card
download card share on x share on reddit drop it in your model-drop post
Code Llama: Runs comfortably
for your own README — links back here

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

Also runs on a Apple M4 Pro: Qwen3 · DeepSeek-R1 (distill) · Qwen2.5 · Mixtral 8x7B · Gemma 3

Best models for a Apple M4 Pro · 48GB →

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 M4 Pro · 48GB too:

modelsharesverdict here
Llama 3.1 8kv/128hd ✅ Runs (tight)
Qwen2.5-Coder 8kv/128hd ✅ Runs comfortably
Llama 3.2 8kv/128hd ✅ Runs comfortably
Llama 3.3 8kv/128hd ✅ Runs (tight)
Mistral 7B 8kv/128hd ✅ Runs comfortably
Mistral Small 3 8kv/128hd ✅ Runs comfortably
Mistral Nemo 8kv/128hd ✅ Runs comfortably
Codestral 8kv/128hd ✅ Runs comfortably

…and 9 more — see the Code Llama page.

Check any combo yourself: open the checker →