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Kai Fricke

In part 3 of our generative AI blog series, we dive into a concrete example of how you can use Ray to scale the training of generative AI models.

Figure 4. Training times for number of GPUs

Figure-4

Achieve Faster Stable Diffusion Model Fine-Tuning With Ray AIR

Faster stable diffusion fine-tuning with Ray AIR

Antoni Baum

Eric Liang

Eric Liang headshot

Jun Gong

Richard Liaw

Richard Liaw headshot

Learn the infrastructure challenges for supporting workloads production deployments around foundation models, and how Ray tackles these challenges.

layered-stack

How Ray Solves Generative AI & LLM Infrastructure Challenges

How Ray solves common production challenges for generative AI infrastructure

Amog Kamsetty

XGBoost-Ray

Introducing Distributed XGBoost Training with Ray

PyTorch Lightning Ray

Scaling up PyTorch Lightning hyperparameter tuning with Ray Tune

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.

NVIDIA-Anyscale Logo 1280x680.jpg

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.

blog-anyscale-pincone

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

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Posts by Kai Fricke

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