The efficient frontier of LLM inference

▤ Summary

This article explains the efficient frontier concept as it applies to LLM inference. It breaks down how engineers balance latency, throughput, cost, and quality in production deployments. It compares techniques that shift along the frontier, such as batch sizing, parallelism, and quantization, with techniques that expand the frontier, such as kernel optimization, speculative decoding, and disaggregated prefill/decode. The article frames these methods as practical tools for improving serving efficiency in agentic coding and other high-volume LLM workloads.

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