How to optimize LLM inference in 2026

▤ Summary

This article explains how to optimize LLM inference costs and performance in 2026. It covers batching strategies, KV cache memory behavior, PagedAttention, prefix caching, grouped-query attention, quantization, speculative decoding, and FlashAttention. It also compares practical trade-offs across throughput, latency, GPU memory, and model serving stacks such as vLLM, TGI, and TensorRT-LLM. The piece emphasizes monitoring tools and tuning methods that help teams raise GPU utilization and reduce time to first token and time per output token.

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