Can RTX 4080 SUPER run Llama 3.1 8B Instruct?
Yes — runs locally
~78 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4080 SUPER (16 GB VRAM) handles Llama 3.1 8B Instruct comfortably using the Q8_0 quantization, which fits in 8.4 GB. Expected throughput is around 78 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Meta's 8B parameter instruction-tuned model. Great balance of performance and efficiency for local deployment.
Setup tutorial: Llama 3.1 8B Instruct on RTX 4080 SUPER
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Llama 3.1 8B Instruct on an NVIDIA GeForce RTX 4080 SUPER with Q8_0 quantization for Grade S performance at ~77 tok/sec.
Prerequisites
Before starting, ensure you have at least 16GB of free disk space, a compatible OS (Windows 10/11 or Linux), the latest NVIDIA drivers (version 525.60.12 or later), and CUDA 11.8 installed.
Expected performance
With the Q8_0 quantization, you can expect the model to run at ~77 tok/sec with 8.4GB VRAM in use, leaving 7.6GB of VRAM for context. This allows for a practical context window of around 131072 tokens, making it suitable for long-form text generation tasks.
1. Install runtimeOllama
curl -L https://ollama.ai/install.sh | bash
ollama setup2. Download the model
Download the Q8_0 quantized model (8.0GB file) from Hugging Face.
ollama pull bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Meta-Llama-3.1-8B-Instruct-Q8_0.gguf3. Run it
ollama run Meta-Llama-3.1-8B-Instruct-Q8_0 --interactive
ollama chat Meta-Llama-3.1-8B-Instruct-Q8_04. Optimize for RTX 4080 SUPER
For optimal performance on the NVIDIA GeForce RTX 4080 SUPER with 16GB VRAM, use the --n-gpu-layers flag to offload layers to the GPU. Set --n-gpu-layers to 32 to utilize the available VRAM efficiently. Enable flash attention (--flash-attn) to speed up inference and reduce memory usage. With 16GB VRAM, you can achieve a practical context window of around 131072 tokens while maintaining ~77 tok/sec.
Troubleshooting
Out of memory errors during inference.
Reduce the --n-gpu-layers value to 24 or 16 to free up more VRAM for context.
Slow inference speed.
Ensure that flash attention is enabled with the --flash-attn flag.
Model fails to load.
Verify that the model file has been downloaded correctly and that the Ollama runtime is properly installed.
Alternative runtimes
Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio is a good choice for a graphical interface and easy model management. llama.cpp offers more control over quantization and optimization settings. Jan is lightweight and suitable for quick prototyping. Choose based on your specific needs and preferences.
Other models that run great on RTX 4080 SUPER
FAQ (20)
What GPU do I need to run Llama 3.1 8B Instruct?
To run Llama 3.1 8B Instruct, you need a GPU with at least 5.1 GB of VRAM for the lowest quantization level, up to 17.0 GB for full precision.
Is Llama 3.1 8B Instruct good for coding?
Llama 3.1 8B Instruct is well-suited for coding tasks, offering a good balance of performance and efficiency for generating code and providing programming assistance.
Llama 3.1 8B Instruct vs Llama 3.1 8B?
Llama 3.1 8B Instruct is an instruction-tuned version of Llama 3.1 8B, making it better suited for following user instructions and generating more coherent and contextually relevant responses.
Can I run Llama 3.1 8B Instruct on a Mac?
Yes, you can run Llama 3.1 8B Instruct on a Mac with an M1 or M2 chip, provided you have the necessary VRAM and system resources.
How much VRAM does Llama 3.1 8B Instruct need?
Llama 3.1 8B Instruct requires between 5.1 GB and 17.0 GB of VRAM, depending on the quantization level used.
Is Llama 3.1 8B Instruct censored?
Llama 3.1 8B Instruct is not inherently censored, but it may include content filters to prevent harmful or inappropriate outputs.
Is Llama 3.1 8B Instruct commercial-use allowed?
Llama 3.1 8B Instruct is licensed under the llama3.1 license, which allows for commercial use, but you should review the specific terms to ensure compliance.
Llama 3.1 8B Instruct context length?
Llama 3.1 8B Instruct has a context length of 131,072 tokens, allowing it to handle very long sequences of text.
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