Can RTX 3080 run Llama 3.1 8B Instruct?

Yes — runs locally

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

Your VRAM

10 GB

Model size

Best quant

Q5_K_M

VRAM needed

5.8 GB

The verdict

The RTX 3080 (10 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

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

TL;DR

Run Llama 3.1 8B Instruct on an NVIDIA GeForce RTX 3080 with Ollama. Grade S, Q5_K_M quantization, ~70 tok/sec.

Prerequisites

Before starting, ensure you have at least 15GB of free disk space, a 64-bit version of Windows or Linux, and the latest NVIDIA drivers (version 510.47.03 or later) with CUDA 11.2 or higher installed.

Expected performance

With the Q5_K_M quantization, you can expect ~70 tok/sec and 5.8GB VRAM in use, leaving 4.2GB for context. This allows for a practical context window of up to 65536 tokens, depending on the complexity of the input.

1. Install runtimeOllama

curl -L https://ollama.com/install.sh | bash
ollama setup

2. Download the model

Download the Q5_K_M quantized model (5.3GB file) from Hugging Face.

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

4. Optimize for RTX 3080

For optimal performance on the NVIDIA GeForce RTX 3080 with 10GB VRAM, use the Q5_K_M quantization. Set --n-gpu-layers to 28 to fit within the 10GB limit. Enable flash attention (--flash-attn) to reduce memory usage and improve speed. With these settings, you should achieve ~70 tok/sec with 5.8GB VRAM in use, leaving 4.2GB for context.

Troubleshooting

Out of memory error during inference

Reduce --n-gpu-layers to 24 or enable --cpu-offload to offload some layers to CPU.

Slow inference speed

Ensure flash attention is enabled with --flash-attn and check that your CUDA installation is up to date.

Model fails to load

Verify the integrity of the downloaded model file and try downloading it again using the same command.

Alternative runtimes

Alternative runtimes include LM Studio, llama.cpp, and Jan. Use LM Studio for a more user-friendly interface, llama.cpp for lightweight deployment, and Jan for advanced customization options. However, Ollama provides a balanced approach with good performance and ease of use, making it the recommended choice for the NVIDIA GeForce RTX 3080.

Full Llama 3.1 8B Instruct details →

Other models that run great on RTX 3080

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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