According to AndrewYNg on X, DeepLearning.AI launched a new course, Efficient Inference with SGLang: Text and Image Generation, created with LMSys and RadixArk and taught by Richard Chen of RadixArk. As reported by AndrewYNg, the course targets production LLM cost bottlenecks and latency using SGLang techniques such as kernel fusion, paged attention, continuous batching, and optimized KV cache management for both text and image generation. According to AndrewYNg, the curriculum emphasizes practical deployment patterns for serving large models at scale, highlighting business value through reduced GPU hours, higher throughput per dollar, and improved tail latency—key metrics for inference economics.

According to @soumithchintala and @ying11231, RadixArk has emerged as a new player in the AI infrastructure sector, aiming to make advanced AI infrastructure open and accessible to everyone (source: https://x.com/ying11231/status/1998079551369593222). The platform is being developed by a core team previously behind SGLang, which gained traction as an open-source AI language stack since its public release in January 2024. RadixArk differentiates itself from established AI infrastructure providers by focusing on community-driven development, openness, and elegant engineering. The company is addressing the inefficiency of repeated infrastructure building across the industry by sharing schedulers, compilers, serving engines, and training pipelines as open tools. This approach creates significant business opportunities for organizations seeking scalable, reliable, and collaborative AI deployment infrastructure, potentially accelerating AI adoption and innovation across sectors (source: @soumithchintala on Twitter, Dec 11, 2025).