Code Llama on NVIDIA GeForce RTX 5080 · 16GB VRAM

✅ Runs (tight) — Code Llama 13B @ Q4_K_M
Code Llama 13B at Q4_K_M needs ~14.1 GB (weights 7.3 GB + KV 6.3 GB + overhead 512 MB @ 8K ctx) of your 14.4 GB usable VRAM — fits, but little headroom — close other apps or trim context. Expect ~25–45 tok/s (fast).
context @ Q4_K_M: comfortable to 4K · runs to its full 16K
quantneedsspeed
~ Q8_019.8 GB~6–10 tok/s (usable)
~ Q5_K_M15.4 GB~8–13 tok/s (usable)
✓ Q4_K_M14.1 GB~25–45 tok/s (fast)
$runlocal install codellama:13b
⚠ 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 5080: DeepSeek-R1 (distill) · Qwen2.5 · Qwen3 · Gemma 4 · Gemma 3

Best models for a NVIDIA GeForce RTX 5080 · 16GB 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 5080 · 16GB VRAM too:

modelsharesverdict here
Llama 3.1 8kv/128hd ✅ Runs comfortably
Qwen2.5-Coder 8kv/128hd ✅ Runs (tight)
Llama 3.2 8kv/128hd ✅ Runs comfortably
Llama 3.3 8kv/128hd ❌ Won't fit
Mistral 7B 8kv/128hd ✅ Runs comfortably
Mistral Small 3 8kv/128hd ✅ Runs (tight)
Mistral Nemo 8kv/128hd ✅ Runs (tight)
Codestral 8kv/128hd ✅ Runs (tight)

…and 10 more — see the Code Llama page.

Check any combo yourself: open the checker →