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
LLM inference.
From model input to useful output
A generated answer has a lifecycle: a bounded request, capacity admission, model work, complete output, and validation. These guides examine that lifecycle from integration through model choice and performance measurement. Caching and batching belong in the discussion only when their effects on quality, freshness, and waiting time remain visible. Choose the task and acceptance rule first; then investigate which part of the request path needs improvement.
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.
How to choose HF models for a production-minded application
Turn model cards, task requirements, licensing review, and evaluation fixtures into a defensible shortlist.
Hugging Face API quickstart: from token to a validated response
Connect a model safely with a clear task, protected credentials, bounded requests, and response validation.
LLM caching and batching: optimize without losing correctness
Separate response caching, prefix reuse, and batching while protecting freshness, authorization, and task quality.