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llm-d (Website)
llm-d is a new Kubernetes-native inference stack that provides production-ready deployment recipes for running LLMs across heterogeneous hardware.
What: The platform provides 'Well-Lit Paths'—pre-tested, adaptable deployment patterns for vLLM and SGLang—aimed at solving multi-tenant quality of service, zero-downtime upgrades, and scaling across GPU, TPU, XPU, and CPU clusters.
Why it matters: It addresses the fragmentation of LLM serving stacks by codifying infrastructure best practices into a unified Kubernetes-native approach that promises predictable costs and performance.
Takeaway: If you are managing LLM inference at scale, explore the open-source repository to find a 'Well-Lit Path' corresponding to your specific hardware and latency requirements.
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