$2 Billion: Llama 3 Is Trained on Two 24k NVIDIA H100 GPU Clusters

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Meta has taken a giant leap in advancing its AI infrastructure with the announcement of two groundbreaking AI clusters, each equipped with 24,576 NVIDIA H100 Tensor Core GPUs. This move underscores Meta’s commitment to not just keeping up with but setting the pace in the AI arms race.

The Backbone of Next-Gen AI

These new clusters represent a significant stride forward in hardware capability, providing the computational power necessary for developing and deploying advanced AI models, including the much-discussed Llama 3, the successor to Meta’s Llama 2 language model.

With plans to expand to 350,000 NVIDIA H100 GPUs by the end of 2024, Meta is gearing up for a future where it can harness compute power equivalent to nearly 600,000 H100 GPUs. This infrastructure will form the backbone of Meta’s AI ambitions, from research and development to real-world applications across its suite of products and services.

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State-of-the-Art Network and Storage Solutions

Meta’s new AI clusters are not just about raw GPU power; they also feature cutting-edge network and storage innovations. One cluster utilizes RDMA over Converged Ethernet (RoCE), while the other employs NVIDIA Quantum2 InfiniBand technology, ensuring robust, high-speed interconnects capable of handling vast amounts of data with minimal latency. This is critical for tasks that require extensive data communication between nodes, such as training large-scale AI models.

On the storage front, Meta has implemented a Linux Filesystem in Userspace (FUSE) API backed by their proprietary ‘Tectonic’ distributed storage solution, optimized for flash media. This setup is designed to meet the intensive data and checkpointing demands of large AI workloads, enabling high throughput and exabyte-scale storage. Additionally, a collaboration with Hammerspace has introduced a parallel network file system (NFS) that enhances the development experience by allowing interactive debugging and immediate code changes across thousands of GPUs.

Commitment to Open Compute and AI Innovation

Meta continues to champion open compute and open-source solutions, leveraging platforms like Grand Teton and OpenRack, which are part of the Open Compute Project (OCP).

These efforts demonstrate Meta’s ongoing commitment to fostering innovation and collaboration in the AI field, making these advanced technologies accessible to the broader community and setting new standards for responsible AI development.

Looking Forward

The implications of Meta’s latest infrastructure expansion are vast, promising not only to accelerate the company’s own AI research but also to influence the broader landscape of AI technology. By continuing to push the boundaries of what’s possible in AI hardware and network capabilities, Meta is paving the way for future advancements that could redefine how AI is integrated into our digital lives.

For those of us following these developments, Meta’s infrastructure roadmap is a clear indication that the AI revolution is not just continuingβ€”it’s accelerating. Stay tuned as we keep an eye on how these advancements unfold and what they mean for the future of technology:

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