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This talking signal gives public context for research themes, product direction, policy, or launch framing. High-signal details: How to optimize LLM inference speed and reduce costs in production Announcing our Series F . 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This talking signal gives public context for research themes, product direction, policy, or launch framing. High-signal details: How to optimize LLM inference speed and reduce costs in production Announcing our Series F . 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Learn more Model performance How to optimize LLM inference speed and reduce costs in production Cut LLM inference latency and cost with continuous batching, speculative decoding, quantization, and more. Learn which techniques fit your workload. Authors Chloe Florit Last updated July 23, 2026 Share TL;DR LLM inference costs too much and responds too slowly when GPUs sit idle, repeat work, and move too much data. This post covers some of the most popular techniques to optimize inference: continuous batching, speculative decoding, KV cache reuse, quantization, smarter routing, and more. Your model isn’t slow. Your inference is. A properly optimized model will outperform a frontier model that isn’t. LLM inference has two main phases: prefill, when the model processes the prompt, and decode, when it generates the response one token at a time. In this post, we’ll cover techniques used to optimize both phases to improve latency, throughput, and cost when running AI models in production . 1. 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