How to Run Meta’s Llama-3.3 Locally: A Step-by-Step Guide

Author(s): Hasitha Pathum

Originally published on Towards AI.

This member-only story is on us. Upgrade to access all of Medium.

Meta’s Llama-3.3, the latest multilingual large language model, has captured attention for its cutting-edge capabilities in text generation, instruction following, and multilingual communication. Running Llama-3.3 locally unlocks its full potential for applications like chatbots, content generation, and advanced research assistance. This article walks you through the process of setting up and running Llama-3.3 on your local machine, ensuring optimal performance and usability.

Before diving into the technical setup, here’s a brief overview of Llama-3.3:

Multilingual Capabilities: Supports eight core languages (English, French, German, Italian, Portuguese, Hindi, Spanish, and Thai) and can be fine-tuned for others.Advanced Features: Includes grouped-query attention (GQA) for scalability and a context length of 128k tokens for long-form text processing.Eco-Friendly Training: Meta achieved net-zero emissions during the training process, setting a new standard for sustainability.Applications: Ideal for tasks such as multilingual chatbots, research summarization, content creation, and tool integration.

To run Llama-3.3 locally, ensure your system meets the following requirements:

GPU: NVIDIA GPU with at least 24GB of VRAM (e.g., A100, H100).RAM: Minimum 32GB (64GB recommended for larger datasets).Storage: At least 250GB of free disk space for the model and dependencies.Operating System: Linux (preferred) or Windows with… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI