LLaVA on NVIDIA GeForce RTX 5090 · 32GB VRAM

✅ Runs comfortably — LLaVA 13B @ Q8_0
LLaVA 13B at Q8_0 needs ~20.1 GB (weights 13.2 GB + KV 6.3 GB + overhead 676 MB @ 8K ctx) of your 28.8 GB usable VRAM — plenty of headroom. Expect ~35–60 tok/s (fast).
context @ Q8_0: comfortable to 4K
quantneedsspeed
✓ Q8_020.1 GB~35–60 tok/s (fast)
✓ Q5_K_M15.6 GB~50–80 tok/s (very fast)
✓ Q4_K_M14.3 GB~55–90 tok/s (very fast)
$runlocal install llava: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
LLaVA: Runs comfortably
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 LLaVA'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
Code Llama 32kv/128hd40kv/128hd ✅ Runs (tight)
OLMo 2 7B 32kv/128hd ✅ Runs comfortably

Check any combo yourself: open the checker →