Yi-Coder 9B on NVIDIA GeForce RTX 5080 · 16GB VRAM

✅ Runs comfortably — Yi-Coder 9B @ Q8_0
Yi-Coder 9B 9B at Q8_0 needs ~10.0 GB (weights 8.7 GB + KV 768 MB + overhead 512 MB @ 8K ctx) of your 14.4 GB usable VRAM — plenty of headroom. Expect ~40–70 tok/s (very fast).
context @ Q8_0: comfortable to 16K · runs to its full 128K
quantneedsspeed
✓ Q8_010.0 GB~40–70 tok/s (very fast)
✓ Q6_K8.0 GB~50–85 tok/s (very fast)
✓ Q4_K_M6.2 GB~70–110 tok/s (very fast)
$runlocal install yi-coder:9b
⚠ 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
Yi-Coder 9B: 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 Yi-Coder 9B'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 4kv/128hd ✅ Runs (tight)
StarCoder2 4kv/128hd ✅ Runs (tight)
Yi 1.5 4kv/128hd ✅ Runs comfortably
Qwen3 Coder 30B-A3B 4kv/128hd ⚠️ Partial GPU offload
SmolLM3 3B 4kv/128hd ✅ Runs comfortably

Check any combo yourself: open the checker →