text model · mistral · Windows
Can I run Mixtral 8x7B on Nvidia GeForce RTX 5090 (32GB)?
Yes. Mixtral 8x7B runs on Nvidia GeForce RTX 5090 (32GB) at Q4_K_M (~28.9 GB of ~31 GB usable).
Fits at Q4_K_M (~28.9 GB of ~31 GB usable) but with little headroom, close other apps.
- Q4_K_M needed
- ~28.9 GB
- Usable on device
- ~31 GB
- Device memory
- 32 GB
- Best quant
- Q4_K_M
Run it
Pick your tool. All three load the same Q4_K_M weights.
ollama run mixtral:8x7b llama-cli -hf MaziyarPanahi/Mixtral-8x7B-Instruct-v0.1-GGUF:Q4_K_M lms get MaziyarPanahi/Mixtral-8x7B-Instruct-v0.1-GGUF AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.
- Parameters
- 46.7B (MoE, 12.9B active)
- Q4_K_M size
- 26.49 GB
- Q8_0 size
- 46.22 GB
- Context
- 32k
- Ollama tag
- mixtral:8x7b
- Memory
- 32 GB vram
- Usable for weights
- ~31 GB
- Best runtime
- vLLM (Linux) / Ollama (CUDA)
You could also run
Run Mixtral 8x7B on other hardware
FAQ
Can Nvidia GeForce RTX 5090 (32GB) run Mixtral 8x7B?
Yes. Mixtral 8x7B runs on Nvidia GeForce RTX 5090 (32GB) at Q4_K_M (~28.9 GB of ~31 GB usable).
How much memory does Mixtral 8x7B need?
It is a tight fit on Nvidia GeForce RTX 5090 (32GB). At Q4_K_M the weights are ~26.49 GB; with KV cache and runtime overhead, budget ~28.9 GB at a 4k context. It is a Mixture-of-Experts model (46.7B total / 12.9B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run Mixtral 8x7B on Windows?
LM Studio for a simple setup; Ollama (CUDA) for the most speed. AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.
Sources
Memory figures are estimates. See methodology.