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Anyscale Ray Team

In this blog post, we explore Percy Liang's insightful talk on "Advances in Foundation Models — Technology, Society, and Applications" Read the recap and watch the full session.

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Advances in Foundation Models — Technology, Society, and Applications

Waleed Kadous

Kyle Huang

Wendi Ding

Liguang Xie

Avnish Narayan

Ricky Xu

Anyscale is releasing LLMPerf for benchmarking LLMs on current LLM offerings. See benchmarking results for Anyscale Endpoints vs Fireworks.ai.

End-to-end time

Reproducible Performance Metrics for LLM inference

In this blog post, we explore Shaowei Su's insightful talk on "Optimizing LLM Training with Airbnb's Next-Gen ML Platform at Airbnb" Read the recap and watch the full session.

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Optimizing LLM Training with Airbnb's Next-Gen ML Platform

Goku Mohandas

Philipp Moritz

In this guide, we will learn how to develop and productionize a retrieval augmented generation (RAG) based LLM application, with a focus on scale and evaluation.  

social-rag-based-llm

Building RAG-based LLM Applications for Production

Matt Connor

Robin Singh

Start with the best open source LLMs in minutes. Validate your ideas with a familiar API, fine tune LLMs to get the quality you need, deploy your apps, and repeat.

anyscale-endpoints

Announcing Anyscale Private Endpoints and Anyscale Endpoints Fine-tuning

In this blog post, we explore Edward Oakes & Shreyas Krishnaswamy's insightful talk on "Building Production AI Applications with Ray Serve" Read the recap and watch the full session.

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Building Production AI Applications with Ray Serve

In this blog post, we explore Lingyi Liu & Pablo Delgado's insightful talk on "Heterogeneous Training Cluster with Ray at Netflix" Read the recap and watch the full session.

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Heterogeneous Training Cluster with Ray at Netflix

In this blog post, we explore Jerry Liu's insightful talk on Practical Data Considerations for Building Production-Ready LLM Applications" Read the recap and watch the full session.

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Practical Data Considerations for Building Production-Ready LLM Applications

In this blog post, we explore Harrison Chase insightful talk on "Building Context-Aware Reasoning Applications with LangChain and LangSmith" Read the recap and watch the full session.

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Building Context-Aware Reasoning Applications with LangChain and LangSmith

In this blog post, we explore Joseph Spisak's insightful talk on "Llama: Scaling Up LLMs in an Open Ecosystem." Read the recap and watch the full session.

top 20 ray session joseph spisak meta

Llama, Scaling Up LLMs in an Open Ecosystem

Yi Cheng

Cade Daniel

Chen Shen

In this post, we discuss the importance of speed when loading large language models and what techniques we employed to make it 20x faster. In particular, we use the Llama 2 series of models. We share how you can reduce latency and costs using the Anyscale platform.

loading llms 20x faster

Loading Llama-2 70b 20x faster with Anyscale Endpoints

Akshay Malik

Edward Oakes

Phi Nguyen

ray-serve-ga-blog

Ray Serve: Tackling the cost and complexity of serving AI in production

Learn how Ray Serve & Anyscale solves common challenges of serving AI in production - improving time to market, reducing cost, and ensuring reliability.

Tackling the Cost & Complexity of Serving AI in Production

Jules S. Damji

Richard Liaw

Richard Liaw headshot

ray 2-7 annoucement

Ray 2.7 features major stability improvements to Ray AI Libraries and KubeRay and introduces RayLLM

Stephanie Wang

Scott Lee

Cheng Su

Hao Chen

Eric Liang

Eric Liang headshot

Data loading & preprocessing can easily become the performance bottleneck in ML pipelines. With Ray Data, data loading can be fast, flexible and scalable.

featured image ml training with ray data

Fast, Flexible and Scalable Data Loading for ML Training

Fast, flexible, and scalable data loading for ML training with Ray Data

Yifei Feng

Sriram Sankar

Siddharth Venkatesh

Ameer Haj Ali

Discover how the Anyscale platform accelerates Gen AI & LLM app development, optimizes compute availability, reduces costs and more.

cloud infrastructure for llm and generative ai social

Cloud Infrastructure for LLM and Generative AI | Anyscale

Cloud Infrastructure for LLM and Generative AI Applications

Deploy DeepSeek R1 on Anyscale for full control, seamless scalability, and optimized performance across any cloud or accelerator — without vendor lock-in.

Anyscale + DeepSeek

Deploying DeepSeek R1 on Anyscale

At Ray Summit 2024, we brought together AI experts and practitioners to dive into the future of distributed AI and scalable machine learning.

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Ray Summit 2024: Breaking Through the AI Complexity Wall

Explore the new and improved Anyscale Workspaces. Get advanced observability features so you spend less time on busywork and more time building AI apps.

Anyscale Share Image

Explore Anyscale’s New UX

Anyscale’s New User Experience: A Comprehensive Overview

With Anyscale on AWS Marketplace, you can build, scale, and optimize AI/ML and Generative AI—all within the trusted AWS cloud environment.

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Anyscale Available on AWS Marketplace

Anyscale Now Available on AWS Marketplace and Achieves Generative AI Competency

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Anyscale is the leading AI application platform. With Anyscale, developers can build, run and scale AI applications instantly.

All Posts

Advances in Foundation Models — Technology, Society, and Applications

Reproducible Performance Metrics for LLM inference

Optimizing LLM Training with Airbnb's Next-Gen ML Platform

Building RAG-based LLM Applications for Production

Announcing Anyscale Private Endpoints and Anyscale Endpoints Fine-tuning

Building Production AI Applications with Ray Serve

Heterogeneous Training Cluster with Ray at Netflix

Practical Data Considerations for Building Production-Ready LLM Applications

Building Context-Aware Reasoning Applications with LangChain and LangSmith

Llama, Scaling Up LLMs in an Open Ecosystem

Loading Llama-2 70b 20x faster with Anyscale Endpoints

Ray Serve: Tackling the cost and complexity of serving AI in production

Ray Serve: Tackling the cost and complexity of serving AI in production

Ray Serve: Tackling the cost and complexity of serving AI in production

Ray 2.7 features major stability improvements to Ray AI Libraries and KubeRay and introduces RayLLM

Fast, flexible, and scalable data loading for ML training with Ray Data

Fast, flexible, and scalable data loading for ML training with Ray Data

Cloud Infrastructure for LLM and Generative AI Applications