Can RTX 5090 run Mixtral 8x7B Instruct?
Yes — runs locally
~42 tok/sec · Fast — smooth conversation. Responses feel real-time.
The verdict
The RTX 5090 (32 GB VRAM) handles Mixtral 8x7B Instruct comfortably using the Q4_K_M quantization, which fits in 25.1 GB. Expected throughput is around 42 tokens/second, which feels Fast — smooth conversation. Responses feel real-time. in interactive use. The OG public MoE — 8 experts, 2 active per token, 47 B total / 13 B active. Apache-2.0.
Setup tutorial: Mixtral 8x7B Instruct on RTX 5090
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Mixtral 8x7B Instruct on an NVIDIA GeForce RTX 5090 with Q4_K_M quantization for Grade A performance at ~32 tok/sec.
Prerequisites
Before starting, ensure you have at least 50GB of free disk space, a compatible OS (Windows 10/11 or Linux), and the latest NVIDIA drivers (version 525.60.13 or later) installed along with CUDA 11.8.
Expected performance
With the Q4_K_M quantization, you can expect ~32 tok/sec performance with 25.1GB VRAM in use, leaving 6.9GB of VRAM for context. This allows for a practical context window of up to 16,384 tokens, depending on the complexity of the input.
1. Install runtimeOllama
curl -fsSL https://ollama.ai/install.sh | sh
ollama install2. Download the model
Download the Q4_K_M quantized Mixtral 8x7B Instruct model (24.6GB file) from Hugging Face.
ollama pull TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF:mixtral-8x7b-instruct-v0.1.Q4_K_M.gguf3. Run it
ollama run mixtral-8x7b-instruct-v0.1.Q4_K_M.gguf --n-gpu-layers 32 --flash-attn --tensor-parallelism 2
ollama chat4. Optimize for RTX 5090
For optimal performance on the NVIDIA GeForce RTX 5090 with 32GB VRAM, set --n-gpu-layers to 32 to fully utilize the GPU memory. Enable --flash-attn for faster attention computations and set --tensor-parallelism to 2 to distribute the workload efficiently across the GPU cores.
Troubleshooting
Out of memory errors during inference
Reduce --n-gpu-layers to 24 or 16 to decrease VRAM usage.
Slow inference speed
Ensure --flash-attn is enabled and try increasing --tensor-parallelism to 4 if your GPU supports it.
Model fails to load
Verify that the model file is correctly downloaded and not corrupted. Re-run the 'ollama pull' command.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for more advanced customization or specific use cases. LM Studio offers a graphical interface for easier management, llama.cpp provides more control over quantization and performance tuning, and Jan is suitable for distributed training and large-scale deployments.
Other models that run great on RTX 5090
FAQ (20)
What GPU do I need to run Mixtral 8x7B Instruct?
To run Mixtral 8x7B Instruct, you need a GPU with at least 25.1 GB of VRAM, but 30.5 GB is recommended for optimal performance.
Is Mixtral 8x7B Instruct good for coding?
Mixtral 8x7B Instruct is well-suited for coding tasks due to its large context length of 32,768 tokens and strong language understanding capabilities.
Mixtral 8x7B Instruct vs Llama 3.1 8B?
Mixtral 8x7B Instruct has more parameters (46.7B vs 8B) and a longer context length (32,768 vs 2,048), making it more powerful for complex tasks but requiring more VRAM.
Can I run Mixtral 8x7B Instruct on a Mac?
Yes, you can run Mixtral 8x7B Instruct on a Mac, but you will need a Mac with an M1 or later chip and sufficient VRAM to handle the model's requirements.
How much VRAM does Mixtral 8x7B Instruct need?
Mixtral 8x7B Instruct requires between 25.1 GB and 30.5 GB of VRAM, depending on the quantization level used.
Is Mixtral 8x7B Instruct censored?
No, Mixtral 8x7B Instruct is not censored; it provides uncensored responses based on the input it receives.
Is Mixtral 8x7B Instruct commercial-use allowed?
Yes, Mixtral 8x7B Instruct is licensed under the Apache-2.0 license, which allows for commercial use.
Mixtral 8x7B Instruct context length?
The context length of Mixtral 8x7B Instruct is 32,768 tokens, allowing it to handle very long inputs and maintain context over extended conversations.
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