Can RTX 4080 SUPER run Mistral 7B Instruct v0.3?
Yes — runs locally
~78 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4080 SUPER (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 4080 SUPER
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Mistral 7B Instruct v0.3 on an NVIDIA GeForce RTX 4080 SUPER 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, NVIDIA driver version 525.60 or later, and CUDA 11.8 or later installed.
Expected performance
With the recommended settings, you can expect the model to run at ~87 tok/sec, using approximately 7.7GB of VRAM. The remaining 8.3GB of VRAM provides ample headroom for a practical context window of up to 32K tokens, ensuring smooth and efficient inference.
1. Install runtimeOllama
curl -L https://ollama.com/install.sh | bash
ollama init2. Download the model
Download the Q8_0 quantized version of Mistral 7B Instruct v0.3 (7.2GB file).
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 32 --flash-attn --tensor-parallelism 24. Optimize for RTX 4080 SUPER
For optimal performance on the NVIDIA GeForce RTX 4080 SUPER with 16GB VRAM, set --n-gpu-layers to 32 to utilize most of the GPU memory. Enable --flash-attn for faster attention computation and set --tensor-parallelism to 2 to distribute the workload across multiple cores. This configuration will use approximately 7.7GB of VRAM, leaving 8.3GB for context, allowing for a practical context window of up to 32K tokens.
Troubleshooting
Out of memory error during inference.
Reduce the number of --n-gpu-layers or decrease the context window size.
Low token generation speed.
Ensure that --flash-attn is enabled and try increasing --tensor-parallelism to 4.
Model fails to load.
Verify that the model file is downloaded correctly and try re-running the 'ollama pull' command.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for more advanced customization or different deployment scenarios. LM Studio offers a user-friendly interface, while llama.cpp is ideal for lightweight, embedded systems. Jan is suitable for distributed training and large-scale deployments. However, Ollama provides a balanced approach with ease of use and good performance on the NVIDIA GeForce RTX 4080 SUPER.
Other models that run great on RTX 4080 SUPER
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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