DeepSeek-R1 (distill) on Apple M2 · 16GB

✅ Runs (tight) — DeepSeek-R1 (distill) 8B (Llama) @ Q5_K_M
DeepSeek-R1 (distill) 8B (Llama) at Q5_K_M needs ~6.8 GB (weights 5.3 GB + KV 1.0 GB + overhead 512 MB @ 8K ctx) of your 8.2 GB usable (of 16.0 GB unified memory) — fits, but little headroom — close other apps or trim context. Expect ~9–14 tok/s (usable).
context @ Q5_K_M: comfortable to 4K · runs to 64K · won't fit past that on this rig
quantneedsspeed
~ Q8_09.5 GB~0.8–1 tok/s (slow)
✓ Q5_K_M6.8 GB~9–14 tok/s (usable)
✓ Q4_K_M6.1 GB~10–17 tok/s (usable)
$runlocal install deepseek-r1:8b
runlocal verdict card
download card share on x share on reddit drop it in your model-drop post
DeepSeek-R1 (distill): Runs (tight)
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: Qwen3 · Llama 3.1 · Qwen2.5 · Gemma 3 · Mistral 7B

Best models for a Apple M2 · 16GB →

Architecturally similar

These share DeepSeek-R1 (distill)'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 2kv/128hd4kv/128hd8kv/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)
Codestral 8kv/128hd 🐢 CPU-only (slow)
Llama 3.2 Vision 8kv/128hd ✅ Runs (tight)
Mixtral 8x7B 8kv/128hd ❌ Won't fit

…and 11 more — see the DeepSeek-R1 (distill) page.

Check any combo yourself: open the checker →