Can RTX 5080 run Mistral 7B Instruct v0.3?
Yes — runs locally
~78 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 5080 (16 GB VRAM) handles Mistral 7B Instruct v0.3 comfortably using the Q8_0 quantization, which fits in 7.7 GB. Expected throughput is around 78 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Efficient 7B model from Mistral AI with strong performance for its size.
Setup tutorial: Mistral 7B Instruct v0.3 on RTX 5080
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Mistral 7B Instruct v0.3 on an NVIDIA GeForce RTX 5080 with Q8_0 quantization for Grade S performance at ~87 tok/sec.
Prerequisites
Before starting, ensure you have at least 10GB of free disk space, a 64-bit version of Windows or Linux, and the latest NVIDIA drivers (version 525.60 or later) installed along with CUDA 11.8.
Expected performance
With the Q8_0 quantization, you can expect the model to run at ~87 tok/sec, using approximately 7.7GB of VRAM. The remaining 8.3GB of VRAM will provide ample headroom for a large context window, enabling efficient handling of long sequences.
1. Install runtimeOllama
pip install ollama
ollama config set device cuda2. Download the model
Download the Q8_0 quantized model (7.2GB file) from the Hugging Face repository.
ollama pull bartowski/Mistral-7B-Instruct-v0.3-GGUF:Mistral-7B-Instruct-v0.3-Q8_0.gguf3. Run it
ollama run Mistral-7B-Instruct-v0.3-Q8_0 --n-gpu-layers 16 --flash-attn --context-length 327684. Optimize for RTX 5080
For optimal performance on the NVIDIA GeForce RTX 5080 with 16GB VRAM, use the --n-gpu-layers 16 flag to offload layers to the GPU, enable --flash-attn for faster attention computation, and set --context-length to 32768. This configuration will utilize approximately 7.7GB of VRAM, leaving 8.3GB for context, allowing for a large practical context window.
Troubleshooting
Out of memory error during inference
Reduce the --context-length to 16384 or lower to reduce VRAM usage.
Slow inference speed
Ensure that the --flash-attn flag is enabled and that your CUDA installation is up to date.
Model fails to load
Check that the model file has been downloaded correctly and that the Ollama runtime is properly configured with the correct device settings.
Alternative runtimes
Alternatively, you can use LM Studio or llama.cpp for running this model. LM Studio provides a more user-friendly interface and is suitable for those who prefer a graphical environment. llama.cpp offers more fine-grained control over model parameters and is ideal for advanced users or those requiring specific optimizations. Jan is another option for a lightweight runtime, but it may not offer the same level of performance as Ollama on this GPU.
Other models that run great on RTX 5080
FAQ (20)
What GPU do I need to run Mistral 7B Instruct v0.3?
To run Mistral 7B Instruct v0.3, you need a GPU with at least 4.6 GB of VRAM, but 15.5 GB is recommended for optimal performance, especially for larger contexts or higher precision.
Is Mistral 7B Instruct v0.3 good for coding?
Yes, Mistral 7B Instruct v0.3 performs well in coding tasks, offering accurate code completion and generation, making it a solid choice for developers.
Mistral 7B Instruct v0.3 vs Llama 3.1 8B?
Mistral 7B Instruct v0.3 has fewer parameters than Llama 3.1 8B but offers competitive performance, especially in terms of efficiency and context length, which is 32768 tokens.
Can I run Mistral 7B Instruct v0.3 on a Mac?
Yes, you can run Mistral 7B Instruct v0.3 on a Mac, provided your Mac has a compatible GPU with sufficient VRAM or a powerful CPU for CPU-based inference.
How much VRAM does Mistral 7B Instruct v0.3 need?
Mistral 7B Instruct v0.3 requires between 4.6 GB and 15.5 GB of VRAM, depending on the quantization level used.
Is Mistral 7B Instruct v0.3 censored?
Mistral 7B Instruct v0.3 is not inherently censored, but it follows ethical guidelines to minimize harmful content. Users can customize filters as needed.
Is Mistral 7B Instruct v0.3 commercial-use allowed?
Yes, Mistral 7B Instruct v0.3 is licensed under Apache-2.0, allowing commercial use without restrictions.
Mistral 7B Instruct v0.3 context length?
The context length for Mistral 7B Instruct v0.3 is 32768 tokens, which is significantly longer than many other models, enabling better handling of long documents.
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