llm-d v1.0: State-of-the-art inference, ready for Day 2

llm-d v1.0 is here, and it is the project's most important release so far. In under a year and a half, llm-d has gone from a shared idea among a handful of contributors to the open standard for distributed inference, built by more than 500 contributors from over 25 organizations: proven in production across every major accelerator, running on public clouds and sovereign on-prem fleets alike, and serving open models through both vLLM and SGLang. The founding vision, any model, any accelerator, any cloud, is now something you can deploy. v1.0 is the release where that stack reaches maturity. llm-d already delivered hardware heterogeneity and advanced inference capabilities out of the box: prefix-aware routing, KV cache offloading, prefill/decode disaggregation, and wide expert parallelism. v1.0 brings that performance to the realities of Day 2 operations: serving multiple tenants, balancing competing priorities, upgrading without downtime, and supporting emerging agentic workloads, all within one suite of co-designed components that compose into your platform. Serving a frontier model fast is no longer the hard part. Running it reliably, for many tenants, through upgrades, under real load, is. That is what v1.0 is built for. The defining goal of the 0.x series was reach: inference…