Can RTX 5080 run Mixtral 8x7B Instruct?
Yes — runs locally
~0 tok/sec · Cannot run — insufficient VRAM
The verdict
The RTX 5080 (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 5080
AI-generated, GPU-specific. Verified commands for your exact hardware.
The Mixtral 8x7B Instruct model runs on an NVIDIA GeForce RTX 5080 with a grade D performance, using the Q5_K_M quantization, achieving ~13 tokens per second.
Prerequisites
Before starting, ensure you have at least 30GB of free disk space, a compatible operating system (Windows or Linux), and the latest NVIDIA drivers (version 525.60.13 or later) with CUDA 11.8 installed.
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 limitation, you will have a headroom of -14.5GB, which means you may need to adjust the context window to a practical size that fits within the available VRAM, likely around 8192 tokens.
1. Install runtimeOllama
pip install ollama
ollama config set device cuda2. 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 TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF:mixtral-8x7b-instruct-v0.1.Q5_K_M.gguf
ollama chat --model TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF:mixtral-8x7b-instruct-v0.1.Q5_K_M.gguf4. Optimize for RTX 5080
For optimal performance on the NVIDIA GeForce RTX 5080 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 reduce the number of active experts or the context length to fit within the available VRAM.
Troubleshooting
Out of memory error during inference.
Reduce the context length or the number of active experts. Use the --n-gpu-layers parameter to offload more layers to CPU memory.
Slow inference speed.
Enable flash attention (--flash-attn) and ensure that the latest CUDA drivers are installed.
Model fails to load.
Verify that the model file is downloaded correctly and that the Ollama runtime is properly installed. Check the Ollama logs for any specific errors.
Alternative runtimes
For users who prefer different runtimes, consider LM Studio for a more user-friendly interface, llama.cpp for lower-level control and customization, or Jan for a lightweight, easy-to-deploy solution. Each runtime has its own strengths, but Ollama provides a good balance of ease of use and performance for the NVIDIA GeForce RTX 5080.
Other models that run great on RTX 5080
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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