LLMs are token-in, token-out - but our serving stacks aren't. Tokenization and preprocessing are still locked inside the inference engine, blocking the cache-aware routing and encode/prefill/decode (E/P/D) disaggregation that production deployments demand. To route smart, you need tokens before you reach the backend - and with multi-modal inputs requiring heavy encode-stage preprocessing, this is an architectural imperative, not just an optimization.

In llm-d, we learned this the hard way: three tokenization approaches, three gaps. We're now converging on disaggregated tokenization via vLLM's Renderer API as a gRPC sidecar, and collaborating with the Gateway API Inference Extension community to define the tokens-in-tokens-out interface. For multi-modal workloads, disaggregating preprocessing unlocks independent scaling of encode, prefill, and decode - each with different compute profiles.

Join us to discuss: How should we standardize tokenization and multi-modal preprocessing outside the engine? How does this shape E/P/D disaggregation? What are your pain points? We'll frame the problem from scheduling, vLLM, and gateway perspectives - then open the floor.