Can RTX 5080 run Llama 3.1 8B Instruct?

Yes — runs locally

~78 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

16 GB

Model size

Best quant

Q8_0

VRAM needed

8.4 GB

The verdict

The RTX 5080 (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 5080

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

TL;DR

Run Llama 3.1 8B Instruct on an NVIDIA GeForce RTX 5080 with Ollama. Grade S performance, using Q8_0 quantization, achieving ~77 tok/sec.

Prerequisites

Before starting, ensure you have at least 16GB of free disk space, a 64-bit version of Windows or Linux, and the latest NVIDIA drivers (version 525.60 or later) installed. Additionally, install CUDA 11.8 or later.

Expected performance

With the Q8_0 quantization, you can expect a token generation rate of approximately 77 tok/sec, using 8.4GB of VRAM. The remaining 7.6GB of VRAM provides ample headroom for handling large context windows, making it suitable for tasks requiring extensive context.

1. Install runtimeOllama

pip install ollama
ollama init

2. Download the model

Download the Q8_0 quantized version of Llama 3.1 8B Instruct (8.0GB file) from Hugging Face.

ollama pull bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Meta-Llama-3.1-8B-Instruct-Q8_0.gguf

3. Run it

ollama run Meta-Llama-3.1-8B-Instruct-Q8_0 --n-gpu-layers 32 --flash-attn
ollama chat Meta-Llama-3.1-8B-Instruct-Q8_0

4. Optimize for RTX 5080

For optimal performance on the NVIDIA GeForce RTX 5080 with 16GB VRAM, set --n-gpu-layers to 32 to utilize the GPU efficiently. Enable --flash-attn to speed up attention computations. With 8.4GB VRAM used by the model, you will have approximately 7.6GB of VRAM left for context, allowing for a practical context window of around 131072 tokens.

Troubleshooting

Out of memory error during inference

Reduce the number of layers on the GPU using --n-gpu-layers <lower_number>.

Slow token generation

Ensure --flash-attn is enabled to optimize attention computation.

Model not found

Verify the model path and ensure the model is correctly downloaded using the 'ollama pull' command.

Alternative runtimes

For users preferring different runtimes, consider LM Studio for a more user-friendly interface, llama.cpp for lower-level control, or Jan for a lightweight alternative. Ollama is recommended for its ease of use and performance on the NVIDIA GeForce RTX 5080.

Full Llama 3.1 8B Instruct details →

Other models that run great on RTX 5080

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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