Llama 3.3 on Apple M2 · 16GB

❌ Won't fit — 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) — more than your 8.2 GB usable of 16.0 GB unified. Try a smaller model or a lower quant.
quantneedsspeed
✗ Q8_075.8 GB—
✗ Q5_K_M51.3 GB—
✗ Q4_K_M44.1 GB—
✗ Q3_K_M36.0 GB—
✗ Q2_K28.3 GB—

Won't fit here — what hardware runs Llama 3.3? →

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Llama 3.3: Won't fit
for your own README — links back here

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

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

Best models for a Apple M2 · 16GB →

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 · 16GB too:

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

Check any combo yourself: open the checker →