Why inference speed is the new moat, real-time AI guides, and benchmarks comparing latency, throughput, and cost.
What time to first token and tokens per second actually measure, how to measure them correctly, and a layer-by-layer guide to reducing AI inference latency in production.
A head-to-head comparison of vLLM self-hosted on H100s versus GeneralCompute's managed inference API: full methodology, throughput and latency numbers, and a total cost of operations breakdown.
A step-by-step tutorial for building a voice AI agent with sub-500ms response times. Plus: why General Compute is the only provider fast enough to use reasoning models in a voice pipeline.
Coding agents make dozens of sequential LLM calls per task. Every millisecond of inference latency compounds across each step, making speed the single biggest infrastructure bottleneck for AI-powered developer tools.
Model quality has commoditized. The real competitive advantage in AI is how fast your infrastructure can deliver results. Inference speed is becoming the defining moat for AI-native products.