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Google is enhancing its AI Hypercomputer with optimized recipes designed to streamline the deployment of large AI models like Meta's Llama4 and DeepSeek. This move aims to alleviate the resource-intensive challenges faced by developers and ML engineers when working with these advanced models. The new recipes will facilitate the use of Llama4 Scout and Maverick models, as well as DeepSeek models, on Google Cloud Trillium TPUs and A3 Mega/Ultra GPUs, making these powerful AI tools more accessible and efficient to deploy.
JetStream, Google’s high-throughput inference engine for LLMs on XLA devices, now supports Llama-4-Scout-17B-16E and Llama-4-Maverick-17B-128E inference on Trillium TPUs. New recipes provide steps to deploy these models using JetStream and MaxText on a Trillium TPU GKE cluster. Pathways on Google Cloud simplifies large-scale machine learning computations by enabling a single JAX client to orchestrate workloads across multiple large TPU slices. MaxText now features reference implementations for Llama4 and DeepSeek, offering detailed guidance on checkpoint conversion, training, and decoding processes.
Developers can find these new recipes and resources on the AI Hypercomputer GitHub repository. These optimized recipes promise to simplify the deployment and resource management of Llama4 and DeepSeek models, enabling users to harness the full potential of these advanced AI technologies on Google Cloud's AI Hypercomputer platform. This initiative underscores Google's commitment to providing a robust AI infrastructure and fostering innovation in the open-source AI community.
ImgSrc: storage.googlea
References :
JetStream, Google’s high-throughput inference engine for LLMs on XLA devices, now supports Llama-4-Scout-17B-16E and Llama-4-Maverick-17B-128E inference on Trillium TPUs. New recipes provide steps to deploy these models using JetStream and MaxText on a Trillium TPU GKE cluster. Pathways on Google Cloud simplifies large-scale machine learning computations by enabling a single JAX client to orchestrate workloads across multiple large TPU slices. MaxText now features reference implementations for Llama4 and DeepSeek, offering detailed guidance on checkpoint conversion, training, and decoding processes.
Developers can find these new recipes and resources on the AI Hypercomputer GitHub repository. These optimized recipes promise to simplify the deployment and resource management of Llama4 and DeepSeek models, enabling users to harness the full potential of these advanced AI technologies on Google Cloud's AI Hypercomputer platform. This initiative underscores Google's commitment to providing a robust AI infrastructure and fostering innovation in the open-source AI community.
ImgSrc: storage.googlea
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References :
- AI & Machine Learning: Accelerate your gen AI: Deploy Llama4 & DeepSeek on AI Hypercomputer with new recipes
- github.com: GitHub repository containing TPU recipes for deploying Llama-4-Scout-17B-16E.
- github.com: GitHub - AI-Hypercomputer/maxtext: High throughput and scalable foundation model training.