Codestral on NVIDIA GeForce RTX 5090 · 32GB VRAM

✅ Runs (tight) — Codestral 22B @ Q8_0
Codestral 22B at Q8_0 needs ~24.9 GB (weights 22.0 GB + KV 1.8 GB + overhead 1.1 GB @ 8K ctx) of your 28.8 GB usable VRAM — fits, but little headroom — close other apps or trim context. Expect ~30–50 tok/s (fast).
context @ Q8_0: runs to its full 32K
quantneedsspeed
✓ Q8_024.9 GB~30–50 tok/s (fast)
✓ Q5_K_M17.2 GB~45–75 tok/s (very fast)
✓ Q4_K_M14.9 GB~50–85 tok/s (very fast)
✓ Q3_K_M12.4 GB~60–100 tok/s (very fast)
$runlocal install codestral:22b
⚠ NVIDIA support is best-effort in v0.1 — verify before relying on it.
runlocal verdict card
download card share on x share on reddit drop it in your model-drop post
Codestral: Runs (tight)
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 M4 Pro

Also runs on a NVIDIA GeForce RTX 5090: DeepSeek-R1 (distill) · Qwen2.5 · Qwen3 · Llama 3.1 · Gemma 3

Best models for a NVIDIA GeForce RTX 5090 · 32GB VRAM →

Architecturally similar

These share Codestral's KV-cache geometry — they size memory the same way, so here's how they fit on a NVIDIA GeForce RTX 5090 · 32GB VRAM too:

modelsharesverdict here
Qwen2.5-Coder 8kv/128hd ✅ Runs (tight)
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 (tight)
Mistral Nemo 8kv/128hd ✅ Runs comfortably
Llama 3.2 Vision 8kv/128hd ✅ Runs comfortably
Mixtral 8x7B 8kv/128hd ✅ Runs (tight)

…and 7 more — see the Codestral page.

Check any combo yourself: open the checker →