Anyscale, the company behind the open-source Ray framework, has signed a definitive agreement to join Nscale, a vertically integrated neocloud provider. The deal will expand investment in Ray and the …
Anyscale Inc. today introduced the Anyscale Physical AI Skill, a new workload skill for building robotics and autonomous-driving systems with Ray and Anyscale that generates code, dependency pins, com…
Ray's new Resource Isolation feature, built using Linux kernel control groups (cgroup-v2), delivers up to 1.5x speedup and eliminates node deaths and job failures on previously unstable workloads such…
Ray Data 2.56 introduces memory-aware execution and improved prefetching to reduce out-of-memory failures and unnecessary object spilling in AI data pipelines. The update includes automatic batch size…
Researchers from Anyscale and NVIDIA demonstrated a method to scale robot policy evaluation by disaggregating GPU-heavy simulation and policy inference workloads using Ray and Isaac Lab. The approach …
Ray Serve LLM, in partnership with Google Kubernetes Engine, announced major performance improvements achieving up to 4.4x higher throughput on prefill-heavy workloads and 24x higher on decode-heavy w…
Ray Data LLM, a library for large-scale batch inference, achieves 2x throughput over vLLM's synchronous LLM engine in production-scale workloads by optimizing hardware utilization and providing fault …
Engineers achieved up to 67% cost savings and 2.7x better goodput by using Prefill-Decode disaggregation with Ray and vLLM on AMD MI325X GPUs, separating prefill and decode phases onto dedicated hardw…
Ray Serve LLM and vLLM on AMD MI325X achieve up to 67% cost savings by disaggregating prefill and decode phases in LLM serving, separating them onto dedicated GPUs to eliminate interference and improv…
Alibaba's 1.7B parameter Qwen3-TTS voice cloning model was fine-tuned using Fully Sharded Data Parallel (FSDP) with PyTorch and Ray, demonstrating memory-efficient distributed training across 4 GPUs. …
Anyscale released agent skills for debugging Ray workloads, including /anyscale-platform-fix and /anyscale-platform-inspect, which automate troubleshooting of failing pipelines. A user demonstrated fi…
Adyen trained a Transaction Foundation Model on 51 trillion tokens using Ray, while Xoople, Criteo, and BMW shared their own scaling AI stories at Anyscale's Ray Day London event. The event highlighte…
At Anyscale's Ray Day: NYC event, Torc Robotics reported achieving 90% GPU utilization by consolidating its fragmented multimodal AI stack onto Ray, up from 30-40%. Discord detailed its ML platform ev…
A benchmark by Alluxio and Anyscale shows that using Alluxio as a distributed NVMe cache for Ray Data reduces cross-region training data read times from 4,241 seconds to 208 seconds, a 20x speedup, by…
Anyscale on Azure entered public preview, allowing Azure customers to provision the AI compute platform powered by Ray inside their own tenancy. Co-engineered with Microsoft, the integration inherits …
Anyscale released Agent Skills, a token-efficient tool for building Ray pipelines, and introduced a new maturity model for ML operations. The skills act as first responders across build, deploy, and o…