Serving more LLM requests at once can lower the cost per token while making each user’s response slower. Baseten’s September 2 inference guide uses that tension to explain how teams tune coding-agent deployments.
Its starting setup assumes reusable KV caches, which reuse earlier prompt computations, and routing that sends related requests to the right cache. Smaller batches favor interactive speed; larger batches favor aggregate throughput.
The guide also covers speculative decoding, which guesses upcoming tokens and checks them with the main model, and separating prompt processing from token generation. Both can improve serving efficiency, but their results depend on workload and configuration.
Baseten warns that the cost-speed curve can change abruptly as settings shift. It recommends measuring multiple configurations rather than assuming every adjustment produces smooth gains. A batch-processing job and an interactive coding agent can therefore need different serving settings.