SGLang
SGLang is a high-performance serving framework for large language models and multimodal models. - sgl-project/sglang
SGLang is a high-performance serving framework for large language models and multimodal models. It is designed to deliver low-latency and high-throughput inference across a wide range of setups, from a single GPU to large distributed clusters. Its core features include: SGLang has been deployed at large scale, generating trillions of tokens in production each day. It is trusted and adopted by a wide range of leading enterprises and institutions, including xAI, AMD, NVIDIA, Intel, LinkedIn, Cursor, Oracle Cloud, Google Cloud, Microsoft Azure, AWS, Atlas Cloud, Voltage Park, Nebius, DataCrunch, Novita, InnoMatrix, MIT, UCLA, the University of Washington, Stanford, UC Berkeley, Tsinghua University, Jam Tea Studios, Baseten, and other major technology organizations. As an open-source LLM inference engine, SGLang has become the de facto industry standard, with deployments running on over 400,000 GPUs worldwide. SGLang is currently hosted under the non-profit open-source organization LMSYS. For enterprises interested in adopting or deploying SGLang at scale, including technical consulting, sponsorship opportunities, or partnership inquiries, please contact us at sglang@lmsys.org. Long-term active SGLang contributors are eligible for coding agent sponsorship, such as Cursor, Claude Code, or OpenAI Codex. Email sglang@lmsys.org with your most important commits or pull requests. SGLang is a high-performance serving framework for large language models and multimodal models. There was an error while loading. Please reload this page. There was an error while loading. Please reload this page. There was an error while loading. Please reload this page. There was an error while loading. Please reload this page.
Ray Serve
Based on the social mentions provided, Ray Serve appears to be well-regarded as part of the broader Ray ecosystem for distributed AI and ML workloads. Users appreciate its integration with popular tools like SGLang and vLLM for both online and batch inference scenarios, with new CLI improvements making large model development more accessible. The active community engagement through frequent meetups, office hours, and educational content suggests strong adoption and support, particularly for LLM inference at scale. The mentions focus heavily on technical capabilities and real-world production use cases, indicating Ray Serve is viewed as a serious solution for enterprise-scale AI deployment rather than just an experimental tool.
SGLang
Ray Serve
SGLang
Ray Serve
Pricing found: $100
Only in SGLang (8)
Only in Ray Serve (1)
SGLang
Ray Serve
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SGLang
Ray Serve
