MistralMixtral 8x7B Instruct on NVIDIA GeForce RTX 4090
Yes — RTX 4090 handles Mixtral 8x7B Instruct well at Q3_K_M — 35 tok/s. Solid daily-driver performance on 24 GB VRAM.
Model Size
46.7B
Device VRAM
24 GB
Bandwidth
1008 GB/s
Quantization
Q3_K_M
Performance by Quantization
OwnRig currently has one published compatibility entry for Mixtral 8x7B Instruct on NVIDIA GeForce RTX 4090 at Q3_K_M. This is the best supported pairing we can stand behind today.
| Quantization | Speed | TTFT | Fits in VRAM | Rating | Confidence |
|---|---|---|---|---|---|
| Q3_K_M | 35 tok/s | 300ms | ✓ Yes | Good | estimated |
Notes
Q3_K_M
Q3 at 21GB fits on 24GB VRAM with 3GB headroom. MoE sparsity means speed is good despite quantization level. For Q4 quality, need 36GB+ VRAM (M4 Max).
About Mixtral 8x7B Instruct
Mixtral 8x7B Instruct (46.7B) is a chat, coding, reasoning, multi-purpose model. Mixture-of-Experts model: 46.7B total params but only ~12.9B active per token. Quality closer to a 13B dense model with inference speed to match. Needs more VRAM for the full weight set though.
View all Mixtral 8x7B Instruct hardware options →About NVIDIA GeForce RTX 4090
NVIDIA GeForce RTX 4090 has 24 GB at 1008 GB/s. Street price: $1,799.
See all models NVIDIA GeForce RTX 4090 can run →Builds with NVIDIA GeForce RTX 4090
Estimate method: MoE model benchmarks. Reference hardware source: github.com (2026-01-15)
Performance varies by driver version, inference engine, quantization method, context length, and system configuration. Figures shown are estimates based on community benchmarks and may not reflect your exact setup. Product names are trademarks of their respective owners. OwnRig is independent and not affiliated with any hardware or AI model provider.