Can RTX 5070 Ti run Mixtral 8x7B Instruct?
Yes — runs locally
~0 tok/sec · Cannot run — insufficient VRAM
The verdict
The RTX 5070 Ti (16 GB VRAM) handles Mixtral 8x7B Instruct comfortably using the Q5_K_M quantization, which fits in 30.5 GB. Expected throughput is around 0 tokens/second, which feels Cannot run — insufficient VRAM 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 5070 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Mixtral 8x7B Instruct on an NVIDIA GeForce RTX 5070 Ti with Q5_K_M quantization. Expect ~13 tok/sec performance, suitable for interactive use.
Prerequisites
Before starting, ensure you have at least 30GB of free disk space, a 64-bit version of Windows or Linux, NVIDIA driver version 525.60.13 or later, and CUDA 11.8 or later installed.
Expected performance
With the Q5_K_M quantization, you can expect the model to run at approximately 13 tokens per second, using 30.5GB of VRAM. With 16GB of VRAM, you will have about 14.5GB of headroom for context, allowing for a practical context window of around 16,000 tokens.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the Mixtral 8x7B Instruct model with Q5_K_M quantization (30.0GB file).
ollama pull TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF:mixtral-8x7b-instruct-v0.1.Q5_K_M.gguf3. Run it
ollama run mixtral-8x7b-instruct-v0.1.Q5_K_M.gguf --n-gpu-layers 32 --flash-attn --context-length 327684. Optimize for RTX 5070 Ti
For optimal performance on the NVIDIA GeForce RTX 5070 Ti with 16GB VRAM, set --n-gpu-layers to 32 to utilize most of the GPU memory. Enable --flash-attn to speed up attention calculations. Given the 30.5GB VRAM requirement, you will have about 14.5GB of VRAM left for context, which allows for a practical context window of around 16,000 tokens.
Troubleshooting
Out of memory error during inference
Reduce the --n-gpu-layers value to 24 or 16 to lower VRAM usage.
Slow inference speed
Ensure that --flash-attn is enabled and check your CPU and disk I/O performance.
Model not loading
Verify that the model file is fully downloaded and not corrupted. Try re-downloading the model.
Alternative runtimes
Consider using LM Studio for a more user-friendly interface, llama.cpp for more advanced quantization options, or Jan for better multi-GPU support. Choose based on your specific needs and hardware configuration.
Other models that run great on RTX 5070 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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