Can RTX 3090 Ti run Llama 3.1 8B Instruct?

Yes — runs locally

~60 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

24 GB

Model size

Best quant

Q8_0

VRAM needed

8.4 GB

The verdict

The RTX 3090 Ti (24 GB VRAM) handles Llama 3.1 8B Instruct comfortably using the Q8_0 quantization, which fits in 8.4 GB. Expected throughput is around 60 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 3090 Ti

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

TL;DR

Run Llama 3.1 8B Instruct on an NVIDIA GeForce RTX 3090 Ti with Grade S performance, using the Q8_0 quantization for ~116 tok/sec.

Prerequisites

Before starting, ensure you have at least 16GB of free disk space, a 64-bit version of Windows or Linux, the latest NVIDIA drivers (version 525.60.13 or later), and CUDA 11.8 installed.

Expected performance

With the Q8_0 quantization, you can expect the model to run at approximately 116 tokens per second, consuming around 8.4GB of VRAM. The remaining 15.6GB 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, which is 8.0GB in size.

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

3. Run it

ollama run Meta-Llama-3.1-8B-Instruct-Q8_0 --context-length 131072
ollama chat

4. Optimize for RTX 3090 Ti

For optimal performance on the NVIDIA GeForce RTX 3090 Ti with 24GB VRAM, use the --n-gpu-layers flag to offload layers to the GPU. Enable flash attention (--flash-attn) for faster inference and set tensor parallelism (--tensor-parallel-size) to 1. This configuration will utilize 8.4GB of VRAM, leaving 15.6GB available for context, allowing for a practical context window of up to 131072 tokens.

Troubleshooting

Out of memory error during inference

Reduce the context length or increase the number of CPU layers using the --n-cpu-layers flag.

Slow inference speed

Ensure that flash attention is enabled with the --flash-attn flag and that the CUDA toolkit is correctly installed and up-to-date.

Model fails to load

Verify that the model file was downloaded correctly and that the Ollama runtime is properly installed. Try re-downloading the model or reinstalling Ollama.

Alternative runtimes

For users preferring different runtimes, consider LM Studio for a more user-friendly interface, llama.cpp for fine-grained control over quantization and performance tuning, or Jan for lightweight deployment. Each has its own strengths, but Ollama is recommended for its ease of use and robust performance on the NVIDIA GeForce RTX 3090 Ti.

Full Llama 3.1 8B Instruct details →

Other models that run great on RTX 3090 Ti

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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