Anyscale
Anyscale Debuts GPU Health Observability for AI Workloads
Anyscale introduces GPU Health Observability, bridging hardware and ML workloads with real-time monitoring to reduce failures.
Anyscale Scales Ray Core to 10,000 Nodes for AI Workloads
Anyscale pushes Ray Core scalability, achieving 10,000-node clusters for AI training and inference. Key improvements include faster actor launches and reduced bottlenecks.
Anyscale to Merge with Nscale, Boosting AI Infrastructure
Anyscale and Nscale unite to advance AI infrastructure, focusing on open-source Ray, GPU capacity, and multi-cloud flexibility.
Ray Data 2.56 Enhances AI Pipelines with Zero OOM Errors
Ray Data 2.56 eliminates OOM errors, reduces memory pressure, and improves AI training/inference speeds by over 50%.
Anyscale Launches Debugging Skills to Streamline Ray and vLLM Fixes
Anyscale's new debugging tools simplify fixing Ray and vLLM workloads, saving hours of manual effort for developers.
Anyscale on Azure Enters Public Preview for Scalable AI
Anyscale, powered by Ray, launches on Azure in public preview, enabling enterprises to scale AI workloads with native Azure integration.
Anyscale Launches LLM Post-Training Tool to Simplify Fine-Tuning
Anyscale unveils a post-training skill for large language models, streamlining methodology selection, GPU planning, and configuration generation.
Anyscale Launches Persistent Ray Dashboards for Debugging AI Workloads
Anyscale introduces new Cluster and Actor dashboards for Ray, offering full data persistence and enhanced debugging for distributed AI workloads.
Anyscale Launches Agent Skills to Streamline AI on Ray
Anyscale's new Agent Skills enhance AI coding tools like Claude Code and Cursor, optimizing Ray-based workflows for speed and scalability.
Ray Serve Upgrade Delivers 88% Lower Latency for AI Inference at Scale
Anyscale announces major Ray Serve optimizations with HAProxy and gRPC, achieving 11.1x throughput gains for LLM inference workloads on enterprise deployments.