Llama 3.1 405b model is HERE | Hardware requirements

TECHNO PREMIUM
23 Jul 202411:58

TLDRThe video discusses the release of the Llama 3.1 AI model, highlighting its various versions, including the new 405 billion parameter model. It emphasizes improvements in performance over previous versions and new features like multi-language support and image creation capabilities. The script also details the hardware requirements for running the models, noting the significant computational and storage needs, especially for the 405 billion model. It guides viewers on how to download the models and touches on the challenges of using cloud-based AI services due to high demand.

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 substantial storage space, approximately 780 GBs, and significant computational resources.
  • 📈 Llama 3.1 shows improvements over Llama 3, with higher scores on the MLU benchmark for each model size.
  • 🌐 A key feature of Llama 3.1 is the incorporation of multiple languages, covering Latin America and enabling image creation with the model.
  • 🔗 To download the models, one must visit Llama meta AI, provide personal information, and follow a link to a GitHub repository for cloning.
  • 🔍 The 405 billion model has multiple deployment options, including MP16, MP8, and FP8, each with different hardware requirements.
  • 💻 Running the MP16 version requires at least two nodes with 8 A100 GPUs each, making it nearly impossible for an average person to run.
  • 🛠️ The FP8 version is quantized and can be served on a single server with A100 GPUs, making it more accessible for inference tasks.
  • 🔍 The script provides detailed instructions on how to navigate the GitHub repository and initiate the model download process.
  • 🔄 The video creator plans to quantize the 405 billion model to reduce its size and performance requirements, making it more usable for a wider range of hardware.
  • 🌐 Online options like Gro AI are mentioned, which offer an API endpoint for using the model, but high demand can lead to usability issues.

Q & A

  • What new model version of Llama was released recently?

    -Llama 3.1 was recently released, which includes different model versions such as 8 billion, 70 billion, and the new 405 billion model.

  • What are the improvements in Llama 3.1 compared to the previous version?

    -Llama 3.1 has improved performance on the same dataset, with higher scores in MLU for the 8 billion, 70 billion, and 405 billion models compared to Llama 3.

  • What is the main feature of Llama 3.1 that supports multiple languages?

    -Llama 3.1 incorporates multiple languages, covering Latin America and allowing users to create images with the model.

  • How much space is required to download the 405 billion model of Llama 3.1?

    -To download the 405 billion model, approximately 780 GBs of storage space is required.

  • What are the minimum hardware requirements to run the 405 billion model of Llama 3.1?

    -The minimum requirement to run the 405 billion model is two nodes with 8 A100 GPUs each, totaling 16 GPUs.

  • What is the difference between the mp16 and mp8 versions of the 405 billion model?

    -The mp16 version uses full BF16 weights and requires two nodes with 8 GPUs each, while the mp8 version uses BF6 weights and can run on a single node with 8 GPUs.

  • What is the fp8 version of the 405 billion model and why is it faster?

    -The fp8 version is a quantized version of the weight, which is faster for inference due to the specific Transformer engine on the H100 chip designed for FP8.

  • How can one download the Llama 3.1 models from the official website?

    -To download the models, one needs to visit Llama meta AI, click download, provide the necessary information, and follow the link provided to clone the GitHub repository.

  • What is the issue with trying to use the 405 billion model on cloud platforms like Gro AI?

    -The issue is the high demand and limited availability, causing delays and making it difficult to get immediate responses or outputs.

  • What is the plan for the next video regarding the 405 billion model?

    -The plan is to download the fp8 version of the 405 billion model on an H100 server, quantize it to reduce its size, and then upload it for others to use on their own hardware.

  • Why might quantizing the 405 billion model be a better option for some users?

    -Quantizing the model reduces its size, making it possible to run on less powerful hardware, although there may be a trade-off in performance.

Outlines

00:00

🚀 Release of Lama 3.1 Models

The script introduces the release of Lama 3.1, which includes three model versions: 8 billion, 70 billion, and the new 405 billion parameters. The narrator shares that the 405 billion model requires significant storage and computational power. The script details the performance improvements of these models over Lama 3, highlighting the 8 billion model's 73 mlu score compared to the previous 65, and the 70 billion model's 86 mlu score compared to 80. The 405 billion model scores 88 mlu. The narrator suggests the 70 billion model might be the best choice for its balance of performance and computational requirements. The script also mentions the incorporation of multiple languages and the ability to create images with the model. Finally, the narrator explains the process of downloading the model from the Lama meta AI website, emphasizing the need for a GitHub repository and the 24-hour limit to download the models.

05:02

🔍 Exploring Model Quantization and Downloading Process

This paragraph delves into the concept of model quantization, explaining how it reduces model size at the cost of some performance. The narrator discusses the availability of quantized versions of the models online and the potential trade-offs involved. The focus then shifts to the process of downloading the models using a provided link, which guides users through cloning a GitHub repository and running a script to download the desired model version. The script also outlines the different deployment options for the 405 billion model, including the mp16, mp8, and fp8 versions, each with its own hardware requirements. The fp8 version, being a quantized model, is highlighted as potentially more accessible for users with h100 GPUs. The narrator plans to quantize the 405 billion model to make it more widely usable and intends to share the quantized version in a future video.

10:03

🌐 Challenges with Model Deployment and Cloud Options

The final paragraph addresses the challenges faced when trying to deploy and use the newly released 405 billion parameter model. The narrator describes the high demand and limited availability of cloud-based options, such as the Gro AI platform, which is currently experiencing difficulties due to its popularity. The script mentions the inability to get immediate responses from these services, suggesting that they are currently unusable. The narrator also compares different AI services, including Meta AI, and expresses the intention to test the 405 billion model on an h100 server, with plans to quantize it for broader accessibility. The paragraph concludes with an invitation for viewers to share their experiences with the new model and an anticipation of demonstrating its capabilities in an upcoming video.

Mindmap

Keywords

💡Llama 3.1

Llama 3.1 refers to the latest version of a language model, which is a type of artificial intelligence designed to understand and generate human-like text. In the video, Llama 3.1 is presented as an upgrade with different model sizes, including 8 billion, 70 billion, and 405 billion parameters, with the latter being the focus due to its newness and size.

💡Hardware requirements

Hardware requirements pertain to the specifications of physical components needed to run a particular software or model effectively. In the context of the video, the 405 billion model of Llama 3.1 demands substantial computational resources and storage space, indicating that it is not feasible for average users to run it on their personal computers.

💡Model parameters

Model parameters are variables that the model learns from the training data to make predictions or generate outputs. The video discusses different versions of the Llama model with varying numbers of parameters, suggesting that the more parameters a model has, the more complex and potentially powerful it is, but also the more resources it requires.

💡Quantization

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

💡GPUs

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

💡Model parallelism

Model parallelism is a technique used in deep learning where a model is split across multiple devices to leverage their combined computational power. The video script discusses MP16 and MP8, which refer to model parallelism with 16 and 8 GPUs respectively, as ways to deploy the large Llama 3.1 model.

💡Inference

Inference in AI refers to the process of using a trained model to make predictions or decisions based on new input data. The video mentions that certain versions of the Llama model are optimized for inference, which is crucial for applications where the model needs to respond quickly to user queries.

💡MLU

MLU stands for 'Machine Learning Unit', which is a hypothetical unit of measurement for the performance of machine learning models. The script uses MLU to compare the performance improvements between different versions of the Llama model.

💡API

API stands for 'Application Programming Interface', which is a set of rules and protocols that allows different software applications to communicate with each other. The video mentions Gro AI, which provides an API endpoint for using the Llama model, highlighting an alternative to running the model locally.

💡Language coverage

Language coverage refers to the range of languages that a model can understand and generate text in. The video script mentions that Llama 3.1 incorporates multiple languages, covering Latin America and providing a more inclusive AI tool.

💡Image generation

Image generation is the ability of a model to create visual content based on textual descriptions. The video highlights a new feature of Llama 3.1 where the model can be instructed to generate images, showcasing the model's versatility beyond text.

Highlights

Llama 3.1 model with 405 billion parameters has been released, alongside 8 billion and 70 billion parameter versions.

The 405 billion model requires significant storage and computational resources.

The 405 billion model's minimum requirement is two nodes with 16 A100 GPUs to serve.

The 70 billion model is recommended for users with two GPUs, as it's more accessible.

Llama 3.1 shows significant improvements in performance compared to Llama 3.

Multiple languages are now supported, covering Latin America and other regions.

The model can create images, a new feature in Llama 3.1.

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

The 405 billion model requires approximately 780GB of storage.

Different deployment options are available, including mp16, mp8, and fp8.

The fp8 version is quantized for faster inference on specific hardware like the H100.

Quantizing the model may lead to performance loss, making the 70 billion model a better choice.

Instructions for downloading the model are provided through a unique link with a 24-hour limit.

The video demonstrates how to navigate the GitHub repository to download the model.

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

Gro AI offers an API endpoint for using the Llama model, but high demand may affect availability.

The presenter will test the model on different hardware configurations in a follow-up video.

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