Benchmarking Hardware for Real Workloads Benchmark numbers are everywhere because they compress a complicated systems story into one line. The trouble is that hardware is not being purchased for a benchmark. It is being purchased to hit a service-level objective, a training deadline, a budget target, and a reliability bar, all at the same time. […]

Hardware Monitoring and Performance Counters AI workloads spend money when hardware is busy and waste money when it is waiting. Monitoring is the practice of making that difference visible in time to act. Performance counters are the vocabulary of that visibility: direct signals from devices, kernels, interconnects, and operating systems that describe what the machine […]

Latency-Sensitive Inference Design Principles Latency-sensitive inference is where model performance stops being a research score and becomes a service contract. A user does not experience average tokens per second. They experience how long it takes for a response to begin, how smoothly it streams, and whether it stalls at the worst possible moment. Most of […]

Serving Hardware Sizing and Capacity Planning Modern AI systems rarely fail because a model is unavailable. They fail because capacity is misread: tokens are cheaper than expected until a spike arrives, latency looks fine until the tail collapses, an innocuous feature doubles average context length, or a queue forms and never drains. Serving is not […]

Training vs Inference Hardware Requirements Training and inference both run neural networks, but they stress hardware in different ways and reward different design choices. Training is a throughput game with large working sets, heavy communication, and long-running jobs. Inference is a service game, where latency, cost per output, and reliability under variable load matter as […]