Can RTX 5060 Ti run Mixtral 8x7B Instruct?
Yes — runs locally
~0 tok/sec · Cannot run — insufficient VRAM
The verdict
The RTX 5060 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 5060 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Mixtral 8x7B Instruct on an NVIDIA GeForce RTX 5060 Ti with Q5_K_M quantization for ~13 tok/sec performance. Grade D, usable.
Prerequisites
Before starting, ensure you have at least 30GB of free disk space, a 64-bit version of Windows or Linux, the latest NVIDIA drivers (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 around 30.5GB of VRAM. Given the 16GB VRAM of the RTX 5060 Ti, you will have about -14.5GB of headroom for context, which means you can practically achieve a context window of around 10,000 tokens before running into VRAM limitations.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the Q5_K_M quantized Mixtral 8x7B Instruct model (30.0GB file size) from Hugging Face.
ollama pull TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF:mixtral-8x7b-instruct-v0.1.Q5_K_M.gguf3. Run it
ollama run TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF --model mixtral-8x7b-instruct-v0.1.Q5_K_M.gguf
ollama chat4. Optimize for RTX 5060 Ti
For optimal performance on the NVIDIA GeForce RTX 5060 Ti with 16GB VRAM, use the --n-gpu-layers parameter to offload some layers to CPU memory. Enable flash attention (--flash-attn) to reduce VRAM usage and improve speed. Given the 16GB VRAM limit, you may need to set --n-gpu-layers to around 20-30 to balance between performance and memory usage.
Troubleshooting
Out of memory error during inference
Reduce the number of GPU layers by increasing --n-gpu-layers, e.g., --n-gpu-layers 30.
Slow inference speed
Enable flash attention with --flash-attn and ensure your CUDA installation is up to date.
Model fails to load
Verify that the model file is correctly downloaded and not corrupted. Try re-downloading the model.
Alternative runtimes
Consider using LM Studio or llama.cpp for more fine-grained control over model parameters and optimizations. Jan is another runtime that offers advanced features but may require more setup. Use these alternatives if you need more customization or better performance on this GPU.
Other models that run great on RTX 5060 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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