Yi-Coder 9B on NVIDIA GeForce RTX 4070 · 12GB VRAM

✅ Runs (tight) — 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 10.8 GB usable VRAM — fits, but little headroom — close other apps or trim context. Expect ~20–35 tok/s (fast).
context @ Q8_0: runs to its full 128K
quantneedsspeed
✓ Q8_010.0 GB~20–35 tok/s (fast)
✓ Q6_K8.0 GB~25–45 tok/s (fast)
✓ Q4_K_M6.2 GB~35–60 tok/s (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 (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 4070: Qwen3 · DeepSeek-R1 (distill) · Qwen2.5 · Llama 3.1 · Gemma 4

Best models for a NVIDIA GeForce RTX 4070 · 12GB 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 4070 · 12GB VRAM too:

modelsharesverdict here
Qwen2.5-Coder 4kv/128hd ✅ Runs (tight)
StarCoder2 4kv/128hd ✅ Runs (tight)
Yi 1.5 4kv/128hd ✅ Runs (tight)
Qwen3 Coder 30B-A3B 4kv/128hd 🐢 CPU-only (slow)
SmolLM3 3B 4kv/128hd ✅ Runs comfortably

Check any combo yourself: open the checker →