AI Performance
LLM latency & throughput, token economics, GPU serving, RAG and vector-DB perf.
Measuring LLM Inference Performance: Latency, Throughput, and Cost
The metrics that actually matter for LLM serving — TTFT, TPOT, tokens/sec, and cost per request — how they trade off, and how to load-test an inference endpoint.
Read →Continuous Batching: How Modern LLM Servers Achieve High Throughput
How continuous batching differs from static batching, why it's central to vLLM and TGI's throughput advantage, and what it costs individual requests.
Read →Prompt Caching and KV Cache: Why Repeated Context Gets Cheaper
How prompt/KV caching reduces cost and latency for repeated context in LLM applications, and when it actually helps versus doesn't.
Read →Benchmarking Vector Database Performance for RAG Systems
What actually matters when benchmarking a vector database for retrieval-augmented generation — recall, latency, and indexing trade-offs.
Read →GPU Utilization for LLM Model Serving: What to Actually Measure
Why GPU utilization percentage alone is a misleading metric for LLM serving, and what to measure instead to understand real efficiency.
Read →Quantization and Performance Trade-offs in LLM Serving
How model quantization (INT8, INT4, and similar) trades accuracy for latency, throughput, and memory savings, and how to evaluate the trade-off.
Read →Optimizing RAG Pipeline Latency: Where the Time Actually Goes
A breakdown of where latency accumulates in a retrieval-augmented generation pipeline, and the highest-leverage places to optimize it.
Read →Benchmarking Open-Source LLM Inference Servers: vLLM, TGI, and Ollama
A practical comparison framework for benchmarking vLLM, TGI, and Ollama, and what each is actually optimized for.
Read →Load Testing LLM APIs: A Practical Guide
How to design a load test specifically for LLM APIs, covering realistic prompt distributions, streaming measurement, and concurrency sweeps.
Read →Token Economics 101: Understanding LLM API Cost Structure
How LLM API pricing actually works — input vs output token pricing, why output costs more, and the practical levers for controlling cost.
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