Mixtral 8x7B on NVIDIA GeForce RTX 5090 · 32GB VRAM

✅ Runs (tight) — Mixtral 8x7B @ Q4_K_M
Mixtral 8x7B 8x7B (MoE) at Q4_K_M needs ~28.7 GB (weights 26.4 GB + KV 1.0 GB + overhead 1.3 GB @ 8K ctx) of your 28.8 GB usable VRAM — fits, but little headroom — close other apps or trim context. Expect ~85–140 tok/s (very fast).
context @ Q4_K_M: runs to its full 32K
quantneedsspeed
✗ Q8_049.5 GB—
~ Q5_K_M33.5 GB~25–40 tok/s (fast)
✓ Q4_K_M28.7 GB~85–140 tok/s (very fast)
✓ Q3_K_M23.3 GB~100–170 tok/s (very fast)
$runlocal install mixtral:8x7b
⚠ 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
Mixtral 8x7B: 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 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 Mixtral 8x7B'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
Qwen2.5-Coder 8kv/128hd ✅ Runs (tight)
Llama 3.2 8kv/128hd ✅ Runs comfortably
Llama 3.3 8kv/128hd ✅ Runs (tight)
Mistral 7B 8kv/128hd ✅ Runs comfortably
Mistral Small 3 8kv/128hd ✅ Runs (tight)
Mistral Nemo 8kv/128hd ✅ Runs comfortably
Codestral 8kv/128hd ✅ Runs (tight)
Llama 3.2 Vision 8kv/128hd ✅ Runs comfortably

…and 7 more — see the Mixtral 8x7B page.

Check any combo yourself: open the checker →