New Llama 3.1 is The Most Powerful Open AI Model Ever! (Beats GPT-4)

Meta has just dropped a BombShell in the AI world with their latest release, the Llama 3.1 AI model, and it's pretty groundbreaking. There's a lot to unpack here, so let's get started. Alright, so Meta's been gaining a lot of attention with its Llama models for a while, but with llama 3.1 they've really upped their game. The star of this release is obviously the 405B model, which is touted as the world's largest and most capable OpenAI model, and it's setting some serious benchmarks in the industry. The Llama 3.1 405B model packs a whopping 405 billion parameters. Now, to put that in perspective, parameters are like the brain cells of AI models. The more you have, the smarter and more capable your model can be. Meta trained this model on over 15 trillion tokens, which are essentially fragments of words, phrases, figures and punctuation. This colossal training required the equivalent of 30.84 million GPU hours and and produce the equivalent of 11,390 tons of CO2 emissions. It's a massive undertaking, but the results speak for themselves. Now, this model has been trained on 16,000 Nvidia H100 GPUs. These are top of the line GPUs that are essential for handling the immense computational load required to train such a large model. Despite its size, Meta claims that the Llama 3.1405 B can go head to head with some of the biggest names in AI the like, OpenAI's GPT4 and Anthropic's Claude 3.5 sonnet. That's a bold claim, but Meta's experimental evaluations suggest it's competitive across a range of tasks, from generating pros to answering chat responses. Now, one of the most exciting aspects of Llama 3.1 is its openness. Meta has released this model as open source, which is a big deal in the world of AI. Open open source means that the code and the model are available for anyone to use, modify and improve. This approach has a lot of benefits. For one, it allows for a broader ecosystem of developers and companies to build upon the model, creating new tools, services and applications. It also means that the technology is more accessible and can be used in a wider range of applications, from academic research to commercial products. Meta has also rolled out updated versions of their smaller llama models, the 70B and 8B variants. These models have been upgraded with support for eight languages, English, German, French, Italian, Portuguese, Hindi, Spanish and Thai. They've also been given a much larger context window, now supporting up to 128,000 tokens. A context window is like an AI's short term memory. The bigger it is, the more information the model can hold onto at any given moment. This is particularly useful for tasks like long form summarization or coding assistance where a lot of context is needed to generate accurate responses. One of the challenges with running such a large model like the 405B is the hardware requirements. At 405 billion parameters, it requires roughly 810GB of memory to run at the full 16 bit precision it was trained at. This is more than a single Nvidia DGX H100 system which has eight 8H100 accelerators in a box can handle. To address this, Meta has released an eight bit quantized version of the model, which cuts the memory footprint roughly in half. Quantization is a technique used to reduce the precision of the model's parameters, which makes it more efficient to run without significantly impacting performance. Now let's talk about why this matters for developers and organizations, the open source nature of llama 3.1 means they can train, fine tune and distill their own models. This flexibility is crucial because different organizations have different needs. Some might require small models for on device tasks and classification, while others might need larger models for more complicated tasks. With Llama 3.1 you can take the most advanced models, continue training them with your own data and then distill them down to the optimal size for your specific use case. Meta is also collaborating with a range of companies to grow the broader ecosystem. Amazon, Databricks and Nvidia are launching full suites of services to support developers in fine tuning and distilling their own models. Companies like Grok have built low latency, low cost inference serving for all the new models. The models will be available on all major clouds including aws, Azure, Google, Oracle and more. Companies like Scale AI, Dell and Deloitte are ready to help enterprises adopt Llama and train custom models with their own data. This collective effort will help make Llama the industry standard and bring the benefits of AI to everyone. Meta's commitment to open source is driven by several factors. First, it ensures that they always have access to the best technology and are not locked into a competitor's closed ecosystem. This is crucial for Meta, especially given the constraints they faced with platforms like Apple. By building an open ecosystem, Meta and other companies can innovate more freely without being restricted by arbitrary rules and constraints imposed by closed platforms and second, open sourcing Llama allows for a more competitive AI landscape. AI development is highly competitive and open. Sourcing a model doesn't give away a massive advantage because the field is constantly evolving. By consistently releasing competitive, efficient and open models generation after generation, Meta can ensure that LLAMA remains at the forefront of AI development. And third, selling access to AI models isn't Meta's business model. This means that openly releasing LLAMA doesn't undercut their revenue or ability to invest in research. This is a significant advantage over closed model providers who rely on selling access to their models for revenue. Finally, Meta has a long history of successful open source projects. They've saved billions of dollars by releasing their server, network and data center designs with the Open Compute project and have benefited from the ecosystem's innovations. Projects like Pytorch and React have become industry standards and Meta aims to achieve similar success with llama. Open source AI is also beneficial for the world. It ensures that more people have access to the benefits and opportunities of AI, prevents power from being concentrated in the hands of a few companies, and promotes the even and safe deployment of AI technology across society. There is an ongoing debate about the safety of open source AI models, but Meta believes that open source AI will be safe safer than closed alternatives. Open source allows for greater transparency and scrutiny, which historically has led to more secure software. Now, meta's safety process for LLAMA includes rigorous testing and red teaming to assess potential risks and mitigate them before release. The models are also developed with safety systems like LLAMA Guard, which helps ensure they are used responsibly. Since these models are trained on information already available on the Internet. The starting point for considering harm is whether a model can facilitate facilitate more harm than information that can quickly be retrieved from search engines like Google. Regarding intentional harm, Meta believes that a world where AI is widely deployed is safer because larger actors can check the power of smaller bad actors. Larger institutions deploying AI at scale will promote security and stability across society. Open source promotes this balance by ensuring that everyone has access to similar generations of models. Meta also addresses the geopolitical implications of open source AI. Some argue that closing models is necessary to prevent adversaries like China from gaining access to them. However, Meta believes that this approach will not work and will only disadvantage the US and its allies. Instead, building a robust open ecosystem and working closely with the government and allies will provide a sustainable first mover advantage and ensure that the latest advances are accessible to those who need them Most. The Llama 3.1 release is not just about the models themselves, but also about the broader ecosystem Meta is building. Meta has released a reference system that includes sample apps and components like the Llama Guard 3 safety model and prompt Guard, its prompt injection filter. They're also seeking feedback from industry partners, startups and community members to shape the future of the LLAMA stack. This will eventually form a series of standardized interfaces that define how toolchain components and a Genic application should be built with the goal of making these interfaces the industry standard. Meta's approach to AI development is reminiscent of the open source Linux kernel's victory over proprietary Unix operating systems. Just as Linux gained popularity and became the industry standard by being open, affordable and more advanced over time, Meta believes that AI will develop in a similar way. By investing in open source AI, Meta aims to build a robust ecosystem that will benefit everyone from startups and universities to large enterprises and governments. With the Llama 3.1 release, Meta is not only advancing AI technology, but also promoting a vision of an open and collaborative future. They're committed to enabling as many developers and partners as possible to use LLAMA and building partnerships to offer unique functionality to their customers. This release is a significant step towards making open source AI the industry standard and bringing the benefits of AI to everyone in the world. Alright, if you like this video, please give it a thumbs up and subscribe for more AI updates. Share your thoughts in the comments below. Thanks for watching and I'll catch you in the next one.