Llama 3.3 on Apple M2 Pro · 32GB

🐢 CPU-only (slow) — Llama 3.3 70B @ Q2_K
Llama 3.3 70B at Q2_K needs ~28.3 GB (weights 24.6 GB + KV 2.5 GB + overhead 1.2 GB @ 8K ctx) of your 18.4 GB usable (of 32.0 GB unified memory) — runs on CPU only. Expect ~0.5–0.8 tok/s (slow).
context @ Q2_K: runs to 8K · won't fit past that on this rig
quantneedsspeed
✗ Q8_075.8 GB—
✗ Q5_K_M51.3 GB—
✗ Q4_K_M44.1 GB—
✗ Q3_K_M36.0 GB—
~ Q2_K28.3 GB~0.5–0.8 tok/s (slow)
$runlocal install llama3.3:70b
runlocal verdict card
download card share on x share on reddit drop it in your model-drop post
Llama 3.3: CPU-only (slow)
for your own README — links back here

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

Also runs on a Apple M2 Pro: DeepSeek-R1 (distill) · Mistral Small 3 · Qwen3 · Llama 3.1 · Qwen2.5

Best models for a Apple M2 Pro · 32GB →

Architecturally similar

These share Llama 3.3's KV-cache geometry — they size memory the same way, so here's how they fit on a Apple M2 Pro · 32GB too:

modelsharesverdict here
Qwen2.5-Coder 8kv/128hd ✅ Runs (tight)
Llama 3.2 8kv/128hd ✅ Runs comfortably
Mistral 7B 8kv/128hd ✅ Runs comfortably
Mistral Nemo 8kv/128hd ✅ Runs comfortably
Codestral 8kv/128hd ✅ Runs (tight)
Llama 3.2 Vision 8kv/128hd ✅ Runs comfortably
Mixtral 8x7B 8kv/128hd 🐢 CPU-only (slow)
Phi-4-mini 8kv/128hd ✅ Runs comfortably

…and 6 more — see the Llama 3.3 page.

Check any combo yourself: open the checker →