Llama 3.1 on NVIDIA GeForce RTX 5080 · 16GB VRAM

✅ Runs comfortably — Llama 3.1 8B @ Q8_0
Llama 3.1 8B at Q8_0 needs ~9.5 GB (weights 8.0 GB + KV 1.0 GB + overhead 512 MB @ 8K ctx) of your 14.4 GB usable VRAM — plenty of headroom. Expect ~45–70 tok/s (very fast).
context @ Q8_0: comfortable to 16K · runs to its full 128K
quantneedsspeed
✓ Q8_09.5 GB~45–70 tok/s (very fast)
✓ Q6_K7.6 GB~55–90 tok/s (very fast)
✓ Q5_K_M6.8 GB~60–100 tok/s (very fast)
✓ Q4_K_M6.1 GB~70–120 tok/s (very fast)
$runlocal install llama3.1:8b
⚠ 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
Llama 3.1: 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 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 Llama 3.1'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
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)
Llama 3.2 Vision 8kv/128hd ✅ Runs (tight)

…and 8 more — see the Llama 3.1 page.

Check any combo yourself: open the checker →