LLM latency benchmarking: TTFT, throughput, and the long tail
Measure first content, full completion, quality, and failures without hiding the conditions behind the numbers.
JOURNAL / TOPIC TAG
Performance.
Measure before you make the claim
Performance is meaningful only in relation to a workload, a measurement boundary, and a quality requirement. This collection connects API deadlines, token-generation timing, streaming behavior, hosting choices, and optimization experiments. Keep failures and sample counts next to percentile summaries. Compare accepted work instead of rewarding an incomplete answer or a hidden warm cache. None of these articles offers a provider leaderboard or an unqualified speed guarantee.
Explore the topic hubRelated field notes
5 guides
Inference Providers vs dedicated endpoints: a practical decision guide
Compare hosting routes through workload, cost per accepted task, deployment control, and operating responsibility.
Streaming APIs: choosing SSE or WebSocket with recovery in mind
Choose a transport by communication pattern, then design event parsing, partial states, backpressure, and reconnects.
High frequency API design: deadlines, queues, and reliable throughput
Design an API around useful throughput, bounded queues, operation-aware retries, and tail latency.
LLM caching and batching: optimize without losing correctness
Separate response caching, prefix reuse, and batching while protecting freshness, authorization, and task quality.