Llama 3.1 405b model is HERE | Hardware requirements

TECHNO PREMIUM
23 Jul 202411:58

TLDRThe Llama 3.1 model has been released with new versions including the 405 billion parameter model. It offers improvements over previous versions, supports multiple languages, and can generate images. However, the 405 billion model requires significant computational resources, making it challenging for personal use. Alternatives like the 70 billion model or cloud services are more accessible.

Takeaways

  • 🆕 Llama 3.1 has been released with three model versions: 8 billion, 70 billion, and the new 405 billion parameters model.
  • 📈 The 405 billion model requires significant computational resources and storage space, with a minimum of 780 GB for the non-quantized version.
  • 💡 The new Llama 3.1 models have improved performance compared to the previous Llama 3, with the 8 billion model scoring 73 on MLU, up from 65.
  • 🌐 Llama 3.1 incorporates support for multiple languages, covering Latin America and enabling image creation with the model.
  • 🔗 To download the model, one must visit the Llama Meta AI website, provide personal information, and follow a unique link provided for a 24-hour download period.
  • 🔧 The 405 billion model has multiple deployment options, including MP16, MP8, and FP8, each with different hardware requirements and performance characteristics.
  • 💻 Running the 405 billion model in MP16 mode requires two nodes with a total of 16 A100 GPUs, making it nearly impossible for an average user to run.
  • 🛠️ The FP8 version of the model is quantized for faster inference and can be served on a single server with A100 GPUs, making it more accessible.
  • 🔍 The script discusses the challenges of running the 405 billion model due to its size and the current unavailability of cloud options that support it.
  • 🔄 The presenter plans to quantize the 405 billion model to reduce its size and performance requirements, aiming to make it usable on more common hardware.
  • 🔄 The video also mentions the use of Gro AI, which offers an API endpoint for using the Llama models with their own specialized hardware, but currently faces high demand and limited availability.

Q & A

  • What versions of the Llama 3.1 model have been released?

    -The Llama 3.1 model has been released in 8 billion, 70 billion, and 405 billion parameter versions.

  • What are the improvements in the Llama 3.1 model compared to Llama 3?

    -Llama 3.1 uses the same dataset as Llama 3 but has been tweaked to be smarter and easier to use. For instance, the 8 billion version scores 73 in MLU compared to 65, the 70 billion version scores 86 compared to 80, and the 405 billion version scores 88.

  • What are the storage and computational requirements for running the 405 billion version of Llama 3.1?

    -Running the 405 billion version requires approximately 780 GB of storage and significant computational resources, specifically two servers each with 8 GPUs, preferably A100 or H100 models.

  • How can users download the Llama 3.1 model?

    -Users can download the model from the Llama Meta AI website. They need to provide their name and information to get a download link, which will lead them to the GitHub repository. They can then clone the repository and follow the instructions to download the model.

  • What are the deployment options for the 405 billion version of Llama 3.1?

    -There are multiple deployment options including mp16, mp8, and fp8. The mp16 requires two nodes with 8 GPUs each, mp8 requires a single node with 8 GPUs, and fp8 is optimized for inference on H100 GPUs with faster performance due to quantized weights.

  • What is the minimum hardware requirement to run the 405 billion parameter model?

    -The minimum hardware requirement is two servers, each with 8 GPUs, preferably A100 or H100 models.

  • What additional features does Llama 3.1 support?

    -Llama 3.1 supports multiple languages including Spanish, and can also create images directly.

  • Why might the 70 billion parameter model be a more practical choice for some users?

    -The 70 billion parameter model might be more practical because it requires fewer computational resources, such as two GPUs, compared to the significant resources needed for the 405 billion version.

  • What are the challenges of running the 405 billion parameter model on personal hardware?

    -Running the 405 billion parameter model on personal hardware is challenging due to the need for extensive computational power and memory, which most personal computers lack. It typically requires high-end GPUs and significant storage.

  • How can users try the Llama 3.1 model if they do not have the required hardware?

    -Users can try the Llama 3.1 model through cloud services like Gro AI, which provide the model as an API endpoint. However, these services might have high demand, leading to longer wait times for responses.

Outlines

00:00

🚀 Llama 3.1 Release Overview

Llama 3.1 was released with multiple model versions: 8 billion, 70 billion, and the new 405 billion parameters. The video explains how to download these models, noting that the 405 billion model requires substantial storage (780GB) and computational resources. The improvements in Llama 3.1 include better performance compared to previous versions, with higher mlu scores. The video also mentions that the model now supports multiple languages, including Spanish, and can generate images.

05:02

🔧 Downloading and Running Llama 3.1 Models

The video provides instructions for downloading the Llama 3.1 models from Meta AI's website. Users must enter their details to get a download link. The video details the hardware requirements for different versions of the 405 billion model, such as mp16, mp8, and fp8, each needing significant GPU resources. The fp8 version is highlighted for its faster performance, particularly on Nvidia's h100 GPUs. The video emphasizes the substantial storage and computational power required for running the 405 billion model.

10:03

🌐 Exploring Alternative Versions and Quantization

The video discusses different versions of the Llama 3.1 models that might be available online, such as on Hugging Face, and the concept of quantization, which reduces model size but can affect performance. Quantized models like K8 or K5 are mentioned as potential solutions for those with limited hardware resources. Detailed steps are provided on how to download, clone, and set up the models using GitHub, and the video demonstrates the process on Mac or Linux systems.

💻 Running Llama 3.1 on Limited Hardware

The video explores the challenges of running the 405 billion parameter model on personal hardware and the potential solutions, including using cloud-based services like Gro AI. However, it highlights the limitations and high demand for these services, making them often unusable. The presenter shares plans to download and quantize the model to make it more accessible for users with less powerful hardware, such as Mac computers or less advanced GPUs. The video concludes with a call to viewers to stay tuned for further updates and potential solutions.

Mindmap

Keywords

💡Llama 3.1

Llama 3.1 refers to a new release of an AI model, specifically the 3.1 version. It is part of the theme as the video discusses the features and improvements of this model over its predecessors. The script mentions different versions of the model, including 8 billion, 70 billion, and 405 billion parameters, with the latter being the focus due to its novelty and size.

💡Hardware requirements

Hardware requirements pertain to the computational resources needed to run the AI model effectively. In the context of the video, the 405 billion parameter model has substantial hardware demands, such as significant storage space and powerful GPUs, which are essential for handling the model's complexity and data processing needs.

💡Model versions

Model versions denote the different parameter sizes of the Llama AI, which include 8 billion, 70 billion, and 405 billion parameters. Each version has distinct capabilities and system requirements, affecting its performance and the hardware necessary to run it, as discussed in the video.

💡Quantization

Quantization in the context of AI models refers to the process of reducing the precision of the numbers used in the model to require less computational power and memory. The video mentions quantizing the 405 billion parameter model to make it more accessible for users with less powerful hardware, at the potential cost of some performance.

💡GPUs

GPUs, or Graphics Processing Units, are specialized electronic hardware加速器 designed to handle complex mathematical and graphical calculations. The script discusses the necessity of having multiple high-end GPUs, such as the A100, to run the largest Llama 3.1 model effectively.

💡Model parallel

Model parallel is a technique used in deep learning to distribute a model's parameters across multiple devices, such as GPUs, to leverage their combined computational power. The video explains that the 405 billion parameter model requires model parallelism with 16 GPUs (mp16) for optimal performance.

💡Inference

Inference in AI refers to the process of making predictions or decisions based on a trained model. The video discusses the use of specialized hardware like LPUs (Language Processing Units) for fast inference, contrasting it with the more general-purpose GPUs.

💡API

API stands for Application Programming Interface, which is a set of rules and protocols for building software applications. The video mentions using an API provided by Gro AI, which allows developers to access the Llama 3.1 model's capabilities through an endpoint.

💡Storage

Storage in the context of the video refers to the physical space required on a device to save the AI model's data. The 405 billion parameter model requires approximately 750 gigabytes of storage, highlighting the substantial resources needed for such large models.

💡Deployment options

Deployment options refer to the various methods available for putting an AI model into practical use. The video outlines different versions of the Llama 3.1 model, such as mp16, mp8, and fp8, each with its own deployment requirements and use cases.

💡Multi-language support

Multi-language support indicates the model's ability to understand and process multiple languages, expanding its accessibility and utility. The video mentions that Llama 3.1 incorporates support for various languages, including Spanish, making it more versatile for global users.

Highlights

Llama 3.1, with its 8 billion, 70 billion, and 405 billion model versions, has been released, offering improved capabilities and features.

The 405 billion model is the largest and newest, requiring significant storage and computational power.

Llama 3.1 models show improved performance compared to Llama 3, with scores of 73, 86, and 88 respectively.

The 70 billion model is suggested for those looking for a balance between performance and computational requirements.

Multiple languages are now supported in Llama 3.1, including various Latin American languages.

The model can now create images based on user prompts, a new feature in Llama 3.1.

Downloading the model requires visiting the Llama meta AI website and following a specific procedure.

The 405 billion model demands 780GB of storage, making it challenging for most users to run.

Different deployment options are available for the 405 billion model, including MP16, MP8, and FP8.

MP16 requires two nodes with 16 A100 GPUs, making it highly resource-intensive.

FP8 is a quantized version of the model, optimized for faster inference on specific GPUs like the H100.

Quantizing the model can reduce its size but may lead to performance loss.

Instructions for downloading the model are provided, including cloning the GitHub repository.

The video will demonstrate the process of downloading and potentially quantizing the 405 billion model.

Online options for trying the model are limited due to high demand and server capabilities.

Gro AI offers an API for using the Llama model, but current demand has made it temporarily unusable.

The presenter plans to quantize the 405 billion model to make it more accessible for users with different hardware capabilities.

Stay tuned for the next video, which will explore running the quantized model on various hardware setups.

Casual Browsing

Llama 3.1 405B is here! (Tested)

2024-07-24 20:32:00

Meta's New AI Model is Here and it BEATS GPT 4o - Llama 3.1 405B Review

2024-07-24 23:13:00

Llama 3.1 better than GPT4 ?? OpenAI vs Meta with Llama 3.1 405B model

2024-07-24 22:21:00

Aider + Llama-3.1 (405B) + NextJS + Supabase : Generate FULL-STACK Apps with Llama-3.1 405B for FREE

2024-07-27 23:15:00

LLAMA-3.1 405B: Open Source AI Is the Path Forward

2024-07-24 23:30:00