@xinyzng: It's interesting to see @MicrosoftAI uses ray actors not just for controller and rollout workers but problem workers fo…
Summary
The tweet discusses Microsoft AI's use of Ray actors for training the MAI-Thinking-1 model, enabling finer granularity for heterogeneous compute and better CPU resource utilization in GPU clusters.
Similar Articles
@raydistributed: Congratulations to the Microsoft AI team on MAI-Thinking-1! Exciting to see Ray used in multiple parts of frontier-mode…
Microsoft AI announces MAI-Thinking-1, a 35B active/1T total MoE reasoning model competitive on STEM and coding tasks, developed using Ray for distributed training and orchestration.
@RayFernando1337: https://x.com/RayFernando1337/status/2070621713952579990
A detailed analysis on whether to run AI models locally or via API, covering hardware options like RTX 5090, RTX PRO 6000, and DGX Spark, with emphasis on memory vs bandwidth trade-offs, cost considerations, and privacy needs.
The AI war is moving from models to machines and I don’t think enough people are talking about it
A commentary arguing that the AI competition is shifting from model quality to hardware placement and infrastructure, highlighting Microsoft's Project Solara, NVIDIA's RTX Spark, and ByteDance's custom CPU efforts as signs that agentic workloads are driving new silicon and deployment strategies.
@anyscalecompute: Most agent frameworks solve orchestration and leave infrastructure completely unresolved. New blog: production-ready AI…
Anyscale published a technical guide on deploying production-ready AI agents using Ray Serve, MCP, and A2A protocols. The article addresses common infrastructure bottlenecks by proposing a decoupled microservices architecture that enables independent scaling of LLMs, tools, and agents.
How Microsoft Ships Thousands of Production AI Agents (18 minute read)
Microsoft shares insights from shipping thousands of production AI agents at enterprise scale, covering the engineering challenges of moving from prototype to production, including the agent harness, retrieval-as-a-subagent, agent identity, and rubric-based evaluation loops.