[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-continuous-batching::en":3,"gloss-cluster-continuous-batching::en":23,"gloss-next-continuous-batching::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"continuous-batching","mlops","Continuous Batching","Continuous batching is an LLM serving technique that schedules work at the iteration level instead of the request level. Static batching waits to assemble a group of requests, runs them together, and can't admit new work until the whole batch finishes — so one long generation holds everyone hostage. Continuous batching re-forms the batch at every decoding step: as soon as any sequence emits its final token, its slot is handed to a waiting request mid-flight. Popularized by Orca and implemented in vLLM, TGI, and TensorRT-LLM, it typically multiplies GPU throughput severalfold at the same latency, because the accelerator is never idling on padding or stragglers. It pairs with paged KV-cache management, since sequences of different lengths must share memory efficiently. For SaaS builders self-hosting open-weight models, choosing a server with continuous batching is often the single biggest lever on cost per token. It is worth being precise about what the technique actually is, because the name invites a wrong reading. Continuous batching is not \"a bigger batch size.\" It is a different scheduling algorithm operating at the iteration level: the server admits arriving requests into, and retires finished ones out of, the active batch on a per-token basis, instead of treating a batch as a fixed group that must be assembled, run, and drained as a unit. That distinction is what lets it absorb the wildly variable generation lengths real traffic produces — one user asking for a word, another for three pages — without the short requests waiting on the long one. Because it is purely a scheduling change, it has no effect whatsoever on output quality; the same weights produce the same distribution, only the accelerator stops idling. It is also not something an end user configures through API parameters. It is serving infrastructure, chosen when you pick or deploy an inference server, and invisible from the client side of a hosted API — though you benefit from it indirectly, since the throughput-per-GPU it unlocks is part of how providers price per-token access at the levels they do while keeping margin. If you self-host, the practical read is that the scheduler matters as much as the model: two deployments of identical weights on identical hardware can differ severalfold in requests served per dollar depending on whether the server re-forms its batch every step. Pair it with paged KV-cache management, since variable-length sequences sharing GPU memory is exactly the condition that makes fragmentation expensive.","Continuous batching re-forms the LLM inference batch every decoding step, slotting new requests in as others finish — multiplying GPU throughput.",null,[11,14,17,20],{"slug":12,"name":13},"inference","Inference",{"slug":15,"name":16},"kv-cache","KV Cache",{"slug":18,"name":19},"model-serving","Model Serving",{"slug":21,"name":22},"throughput","Throughput",[24,28,32,35,38,42,45,48,51,54,57,60],{"slug":25,"category":5,"name":26,"updated_at":27},"annotation-guidelines","Annotation Guidelines","2026-08-24T03:30:02+00:00",{"slug":29,"category":5,"name":30,"updated_at":31},"baseline-model","Baseline Model","2026-08-24T02:46:38+00:00",{"slug":33,"category":5,"name":34,"updated_at":31},"batch-inference","Batch Inference",{"slug":36,"category":5,"name":37,"updated_at":31},"canary-prompt","Canary Prompt",{"slug":39,"category":5,"name":40,"updated_at":41},"champion-challenger","Champion-Challenger (A\u002FB Model Testing)","2026-08-24T02:46:37+00:00",{"slug":43,"category":5,"name":44,"updated_at":31},"class-imbalance","Class Imbalance",{"slug":46,"category":5,"name":47,"updated_at":31},"cross-validation","Cross-Validation",{"slug":49,"category":5,"name":50,"updated_at":31},"data-labeling","Data Labeling",{"slug":52,"category":5,"name":53,"updated_at":41},"drift-detection","Drift Detection",{"slug":55,"category":5,"name":56,"updated_at":41},"eval-harness","Eval Harness",{"slug":58,"category":5,"name":59,"updated_at":41},"experiment-tracking","Experiment Tracking",{"slug":61,"category":5,"name":62,"updated_at":31},"explainability","Explainability"]