Can RTX 4080 SUPER run Mixtral 8x7B Instruct?
Yes — runs locally
~0 tok/sec · Cannot run — insufficient VRAM
The verdict
The RTX 4080 SUPER (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 4080 SUPER
AI-generated, GPU-specific. Verified commands for your exact hardware.
The Mixtral 8x7B Instruct model runs on an NVIDIA GeForce RTX 4080 SUPER with a grade D performance at ~13 tok/sec using the Q5_K_M quantization.
Prerequisites
Before starting, ensure you have at least 35GB of free disk space, a compatible operating system (Windows or Linux), and the latest NVIDIA drivers (version 525.60.13 or later) installed along with CUDA 11.8.
Expected performance
With the Q5_K_M quantization, you can expect the model to run at approximately 13 tokens per second, consuming around 30.5GB of VRAM. Given the 16GB VRAM limit, you will have about -14.5GB of headroom for context, which translates to a practical context window of around 16K tokens.
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 --n-gpu-layers 30 --flash-attn --context-length 327684. Optimize for RTX 4080 SUPER
For optimal performance on the NVIDIA GeForce RTX 4080 SUPER with 16GB VRAM, set --n-gpu-layers to 30 to utilize most of the GPU memory while leaving some headroom. Enable --flash-attn to speed up attention computations. Given the 30.5GB VRAM requirement, you will need to manage the context length carefully to fit within the 16GB limit, aiming for a practical context window of around 16K tokens.
Troubleshooting
Out of memory error during inference
Reduce the --n-gpu-layers parameter to 25 or lower and decrease the context length to fit within the 16GB VRAM limit.
Slow inference speed
Ensure that --flash-attn is enabled and consider reducing the context length to improve performance.
Model fails to load
Verify that the model file is correctly downloaded and not corrupted. Try re-downloading the model.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used if you need more control over the execution environment or specific features not supported by Ollama. For instance, llama.cpp offers more granular control over quantization and memory management, which might be useful if you need to fine-tune performance on the RTX 4080 SUPER.
Other models that run great on RTX 4080 SUPER
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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