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Parallel Requests Beat Combined Prompts for LLMs

A technical analysis argues that for multiple independent questions, sending separate parallel requests to an LLM is almost always faster and often higher-quality than concatenating all questions into one combined prompt. The piece explains that modern LLMs use autoregressive decoding (one token per forward pass), so combined prompts force sequential generation of the total token count, increasing latency and KV-cache attention costs. Server-side continuous batching (used by engines such as vLLM, TensorRT-LLM, and TGI) enables multiple simultaneous requests to be processed in parallel, producing a theoretical speedup roughly equal to the number of parallel requests. The article also covers prefill vs. decode phases, quality risks from combined prompts (attention dilution, format confusion, error propagation), and scenarios where combining may still be preferable (rate limits, network-dominated latency, hidden correlations, or extremely short answers).