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In part 2 of our generative AI blog series, we cover how Ray empowers large language models (LLM) frameworks such as Alpa.

Figure 5: Technical integration layered stack for LLM


Figure-5

High-Performance LLM Training at 1000 GPU Scale With Alpa & Ray

Training 175B Parameter Language Models at 1000 GPU scale with Alpa and Ray

Stephanie Wang

Dmitri Gekhtman

Sang Cho

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Ray 1.13: Improving support for shuffling terabyte-scale and larger datasets

Shreyas Krishnaswamy

Simon Mo

Simon Mo headshot

Edward Oakes

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Multi-model composition with Ray Serve deployment graphs

Ray Summit 2024, the annual Ray community conference, is back in San Francisco, CA this September 30th through October 2nd. It is a two-day event with a third day dedicated to training.

Call for Proposals is open Blog Post

Ray Summit 2024 Call for Proposals is now open

Intel and Anyscale have integrated Ray with Gaudi3. Gaudi3 is AI-optimized for efficient training and other machine learning workloads.

Anyscale - Intel

Accelerating AI: Harnessing Intel(R) Gaudi(R) 3 with Ray 2.10

Anyscale is teaming with NVIDIA to combine the developer productivity of Ray Serve and RayLLM with the cutting-edge optimizations from NVIDIA Triton Inference Server software and the NVIDIA TensorRT-LLM library.

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Low-latency Generative AI Model Serving with Ray, NVIDIA Triton Inference Server, and NVIDIA TensorRT-LLM

Generating embeddings is a critical task for developing successful RAG applications, and Anyscalew with Pinecone is the most cost-efficient solution for this workflow.

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RAG at Scale: 10x Cheaper Embedding Computations with Anyscale and Pinecone

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