Mixtral 8x7B on NVIDIA GeForce RTX 4090 · 24GB VRAM
⚠️ Partial GPU offload — 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 21.6 GB usable VRAM — some layers spill to system RAM. Expect ~15–25 tok/s (usable).
context @ Q4_K_M: runs to its full 32K
| quant | needs | speed |
|---|---|---|
| ✗ Q8_0 | 49.5 GB | — |
| ✗ Q5_K_M | 33.5 GB | — |
| ~ Q4_K_M | 28.7 GB | ~15–25 tok/s (usable) |
| ~ Q3_K_M | 23.3 GB | ~18–30 tok/s (fast) |
$runlocal install mixtral:8x7b
⚠ NVIDIA support is best-effort in v0.1 — verify before relying on it.
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 4090: DeepSeek-R1 (distill) · Qwen3 · Qwen2.5 · Gemma 3 · Gemma 4
Best models for a NVIDIA GeForce RTX 4090 · 24GB 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 4090 · 24GB VRAM too:
| model | shares | verdict here |
|---|---|---|
| Llama 3.1 | 8kv/128hd | ✅ Runs comfortably |
| Qwen2.5-Coder | 8kv/128hd | ✅ Runs (tight) |
| Llama 3.2 | 8kv/128hd | ✅ Runs comfortably |
| Llama 3.3 | 8kv/128hd | ⚠️ Partial GPU offload |
| Mistral 7B | 8kv/128hd | ✅ Runs comfortably |
| Mistral Small 3 | 8kv/128hd | ✅ Runs (tight) |
| Mistral Nemo | 8kv/128hd | ✅ Runs comfortably |
| Codestral | 8kv/128hd | ✅ Runs comfortably |
…and 8 more — see the Mixtral 8x7B page.
Check any combo yourself: open the checker →