Can RTX 4060 Ti 16GB run Mixtral 8x7B Instruct?
Yes — runs locally
~0 tok/sec · Cannot run — insufficient VRAM
The verdict
The RTX 4060 Ti 16GB (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 4060 Ti 16GB
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Mixtral 8x7B Instruct on an NVIDIA GeForce RTX 4060 Ti 16GB with Q5_K_M quantization. Expect ~13 tok/sec, 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 or later, and CUDA 11.8 or later installed.
Expected performance
With the Q5_K_M quantization, you can expect a token generation rate of approximately 13 tok/sec, with 30.5GB VRAM in use. Given the 16GB VRAM limit, you will have about 14.5GB of VRAM headroom for the context, allowing for a practical context window of around 16,000 tokens.
1. Install runtimeOllama
pip install ollama
ollama config set cuda_path /usr/local/cuda2. 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 mixtral-8x7b-instruct-v0.1.Q5_K_M.gguf --n-gpu-layers 30 --flash-attn --context-length 327684. Optimize for RTX 4060 Ti 16GB
For optimal performance on the NVIDIA GeForce RTX 4060 Ti 16GB, use --n-gpu-layers 30 to maximize the number of layers offloaded to the GPU. Enable --flash-attn for efficient attention computation. With 16GB VRAM, you can achieve a practical context window of around 16,000 tokens, given the 30.5GB VRAM requirement of the Q5_K_M quantization.
Troubleshooting
Out of memory errors during inference
Reduce the --n-gpu-layers parameter or decrease the context length using --context-length.
Slow token generation rate
Ensure that --flash-attn is enabled and that your CUDA drivers are up to date.
Model fails to load
Verify that the model file has been downloaded correctly and that there is sufficient disk space available.
Alternative runtimes
Alternative runtimes include LM Studio and llama.cpp. LM Studio offers a more user-friendly interface and is suitable for users who prefer a graphical environment. llama.cpp is a lightweight option for running models directly from the command line and is ideal for low-resource systems. Jan is another runtime that supports a wide range of models but may require additional configuration for optimal performance on this GPU.
Other models that run great on RTX 4060 Ti 16GB
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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