Skip to main content

LLM Inference Optimization and Latency

LLM inference optimization is the discipline of reducing the time between a user's request and the first token of the model's response (latency) while maximizing the number of tokens the model generates per second (throughput). In production systems, a 1-second reduction in time-to-first-token (TTFT) can increase user satisfaction by 15-25%, and throughput bottlenecks directly impact cost per inference. This series guides you from measuring latency to implementing a fully tuned serving stack using streaming, request batching, KV caching, prompt reuse, speculative decoding, and quantization—each technique optimizing a different phase of the inference pipeline.

Why Inference Optimization Matters​

Modern language models like GPT-4, Claude, and Llama are transformer-based neural networks that generate output tokens sequentially. During the prefill phase, the model processes your entire prompt to compute attention weights; during the decode phase, it generates one token at a time. A naive serving setup wastes time on redundant computation, underutilizes hardware parallelism, and forces users to wait 3-10 seconds for the first token. Real production systems (OpenAI, Anthropic, Meta) use every technique in this series to cut TTFT to 50-200ms and handle thousands of concurrent requests.

By the end of this series, you will:

  • Measure and profile inference latency with domain-specific metrics (TTFT, tokens-per-second, time-per-token in decode).
  • Implement request streaming to reduce perceived latency.
  • Batch requests to maximize throughput without harming individual-request latency.
  • Cache attention key-value matrices and encoded prompts to skip redundant computation.
  • Use speculative decoding to parallelize token prediction.
  • Compress models via quantization while maintaining accuracy.
  • Combine all techniques into a coherent, tuned serving architecture.

This series is designed for backend engineers, ML systems engineers, and prompt engineers who need to deploy LLMs responsibly and at scale.

Articles in this Series​