Can RTX 4080 run Llama 3.1 8B Instruct?
Yes — runs locally
~78 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4080 (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
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Llama 3.1 8B Instruct on an NVIDIA GeForce RTX 4080 with Q8_0 quantization for Grade S performance at ~77 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 470 or later, and CUDA 11.2 or later installed.
Expected performance
With the recommended settings, you can expect the model to run at approximately 77 tokens per second, using around 8.4GB of VRAM. This leaves about 7.6GB of VRAM available for context, allowing for a practical context window of up to 131,072 tokens.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the Q8_0 quantized version of Llama 3.1 8B Instruct (8.0GB file size) 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.gguf --n-gpu-layers 32 --flash-attn --tensor-parallelism 24. Optimize for RTX 4080
For optimal performance on the NVIDIA GeForce RTX 4080 with 16GB VRAM, set --n-gpu-layers to 32 to utilize most of the GPU memory. Enable --flash-attn for faster attention computations and set --tensor-parallelism to 2 to distribute the workload efficiently. This configuration ensures that the model runs smoothly within the 16GB VRAM limit.
Troubleshooting
Out of memory error during inference
Reduce the number of --n-gpu-layers or disable --tensor-parallelism to lower VRAM usage.
Slow inference speed
Ensure that --flash-attn is enabled and check if your CUDA installation is up to date.
Model fails to load
Verify that the model file was downloaded correctly and that there are no issues with the Ollama installation.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for more advanced customization or different performance profiles. LM Studio offers a user-friendly interface, while llama.cpp provides more control over quantization and optimization. Jan is suitable for distributed training and large-scale deployments. For the NVIDIA GeForce RTX 4080, Ollama is recommended for its ease of use and efficient performance.
Other models that run great on RTX 4080
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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