How to Fine-Tune Llama 3 on an NVIDIA A100 GPU Server




Meta’s Llama 3 is one of the most powerful open-source Large Language Models (LLMs) available today. However, out of the box, it is a general-purpose model. To make it perform specific enterprise tasks — such as generating accurate medical reports, coding in a proprietary language, or acting as a highly specialized customer support agent — you need to fine-tune it on your own dataset.

Fine-tuning an LLM requires massive computational power and high-bandwidth memory. The NVIDIA A100 (80GB VRAM) is the industry-standard GPU for this task. Deploying this on a bare-metal dedicated server ensures you don’t face the throttling or hidden egress fees common with shared cloud GPUs.

In this tutorial, we will show you how to fine-tune the Llama 3 (8B) model using QLoRA (Quantized Low-Rank Adaptation) on an Ubuntu 24.04 LTS bare-metal NVIDIA A100 server

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Prerequisites

Before starting, ensure you have the following ready:

  • Hardware: An Enterprise GPU Dedicated Server with at least one NVIDIA A100 (80GB) GPU.

  • Operating System: Ubuntu 24.04 LTS (or 22.04 LTS) with root or sudo SSH access.

  • NVIDIA Drivers & CUDA: CUDA Toolkit version 12.1 or higher installed. Verify by running nvidia-smi in your terminal.

  • Hugging Face Account: A Hugging Face account and Access Token to download the Llama 3 gated model weights. Accept the Meta license on the model page.

    Click the link in our bio to read the step-by-step tutorial!
    https://www.gtzhost.com/tutorials/howto/fine-tune-llama-3-nvidia-a100/

Location: United Kingdom

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