Can RTX 3080 Ti run Llama 3.1 8B Instruct?

Yes — runs locally

~46 tok/sec · Fast — smooth conversation. Responses feel real-time.

Your VRAM

12 GB

Model size

Best quant

Q5_K_M

VRAM needed

5.8 GB

The verdict

The RTX 3080 Ti (12 GB VRAM) handles Llama 3.1 8B Instruct comfortably using the Q5_K_M quantization, which fits in 5.8 GB. Expected throughput is around 46 tokens/second, which feels Fast — smooth conversation. Responses feel real-time. 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 3080 Ti

AI-generated, GPU-specific. Verified commands for your exact hardware.

TL;DR

Run Llama 3.1 8B Instruct on an NVIDIA GeForce RTX 3080 Ti with Grade S performance, using the Q5_K_M quantization for ~84 tok/sec.

Prerequisites

Before starting, ensure you have at least 10GB of free disk space, a compatible operating system (Windows or Linux), the latest NVIDIA drivers (version 525.60.13 or later), and CUDA 11.8 installed.

Expected performance

With the Q5_K_M quantization, you can expect ~84 tok/sec performance, utilizing 5.8GB of VRAM, leaving 6.2GB for context. This setup allows for a practical context window of around 64K tokens, making it suitable for long-form text generation tasks.

1. Install runtimeOllama

pip install ollama
ollama init

2. Download the model

Download the Q5_K_M quantized version of the model (5.3GB file).

ollama pull bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Meta-Llama-3.1-8B-Instruct-Q5_K_M.gguf

3. Run it

ollama run Meta-Llama-3.1-8B-Instruct-Q5_K_M.gguf --interactive
ollama chat Meta-Llama-3.1-8B-Instruct-Q5_K_M.gguf

4. Optimize for RTX 3080 Ti

For optimal performance on the NVIDIA GeForce RTX 3080 Ti with 12GB VRAM, use the --n-gpu-layers flag to offload some layers to CPU if needed. Enable flash attention (--flash-attn) to reduce memory usage and improve speed. Given the 5.8GB VRAM requirement, you will have 6.2GB of VRAM left for context, allowing for a practical context window of around 64K tokens.

Troubleshooting

Out of memory errors during inference.

Reduce the number of GPU layers with --n-gpu-layers <N> where <N> is the number of layers to keep on the GPU. For example, --n-gpu-layers 20.

Slow inference speed.

Enable flash attention with --flash-attn and ensure your CUDA installation is up-to-date.

Model fails to load.

Verify the model file integrity and try re-downloading the model using the ollama pull command.

Alternative runtimes

Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for more advanced customization or specific use cases. LM Studio offers a graphical interface and is ideal for users who prefer a GUI. llama.cpp provides more control over quantization and optimization but requires more manual setup. Jan is lightweight and efficient but may lack some features compared to Ollama.

Full Llama 3.1 8B Instruct details →

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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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