Can RTX 3090 Ti run Mixtral 8x7B Instruct?
Yes — runs locally
~17 tok/sec · Good — slight pause, then text streams smoothly.
The verdict
The RTX 3090 Ti (24 GB VRAM) handles Mixtral 8x7B Instruct comfortably using the Q4_K_M quantization, which fits in 25.1 GB. Expected throughput is around 17 tokens/second, which feels Good — slight pause, then text streams smoothly. 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 3090 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Mixtral 8x7B Instruct on an NVIDIA GeForce RTX 3090 Ti with Q4_K_M quantization for a comfortable ~24 tok/sec, achieving a Grade C performance.
Prerequisites
Before starting, ensure you have at least 50GB of free disk space, a compatible OS (Windows 10/11 or Linux), the latest NVIDIA driver (version 510.79 or later), and CUDA 11.4 or later installed.
Expected performance
With the recommended settings, you can expect a throughput of ~24 tok/sec, with 25.1GB of VRAM in use. The remaining -1.1GB of VRAM provides a practical context window of up to 16,384 tokens, depending on the complexity of the input.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the Mixtral 8x7B Instruct model with Q4_K_M quantization (24.6GB file).
ollama pull TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF:mixtral-8x7b-instruct-v0.1.Q4_K_M.gguf3. Run it
ollama run --model TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF --quantization Q4_K_M --n-gpu-layers 48 --flash-attn
ollama interactive4. Optimize for RTX 3090 Ti
For optimal performance on the NVIDIA GeForce RTX 3090 Ti with 24GB VRAM, set --n-gpu-layers to 48 to maximize the use of GPU memory. Enable --flash-attn to reduce memory usage and improve speed. Given the 25.1GB VRAM requirement, you will have approximately -1.1GB of headroom, which is sufficient for smaller context windows but may limit larger ones.
Troubleshooting
Out of memory error during inference
Reduce --n-gpu-layers to 32 or enable --cpu-offload to offload some layers to the CPU.
Low throughput
Ensure that --flash-attn is enabled and try increasing the batch size if your application allows it.
Model fails to load
Verify that the model file is fully downloaded and not corrupted. Re-run the download command if necessary.
Alternative runtimes
For users who prefer a different runtime, consider LM Studio for a more user-friendly interface, llama.cpp for fine-grained control over optimizations, or Jan for a lightweight, easy-to-deploy solution. Each has its strengths, but Ollama provides a balanced approach suitable for most use cases on the RTX 3090 Ti.
Other models that run great on RTX 3090 Ti
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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