Code Llama on NVIDIA GeForce RTX 5090 · 32GB VRAM

✅ Runs (tight) — 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 28.8 GB usable VRAM — fits, but little headroom — close other apps or trim context. Expect ~30–50 tok/s (fast).
context @ Q5_K_M: runs to its full 16K
quantneedsspeed
~ Q8_036.6 GB~6–10 tok/s (usable)
✓ Q5_K_M25.0 GB~30–50 tok/s (fast)
✓ Q4_K_M21.5 GB~35–60 tok/s (fast)
✓ Q3_K_M17.6 GB~45–70 tok/s (very fast)
$runlocal install codellama:34b
⚠ 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
Code Llama: 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 Code Llama'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
Codestral 8kv/128hd ✅ Runs (tight)
Llama 3.2 Vision 8kv/128hd ✅ Runs comfortably

…and 9 more — see the Code Llama page.

Check any combo yourself: open the checker →