Building a Test Plan to Evaluate AI Hardware With Your Own Workloads
Step-by-step plan for evaluating AI hardware yourself: choosing workloads, controlling variables, recording results and making a defensible decision.
AI Chips & Accelerators
GPU, NPU, TPU — how they compare.
AI Chip Technology, GPU vs NPU vs TPU, AI Hardware Architecture, AI Chip Performance & Benchmarking and AI Semiconductor Innovation explained.
Latest guides
Browse categories How to Read AI Chip Benchmarks Without Being Misled
A guide to interpreting AI chip benchmark results: what peak figures hide, which questions to ask about methodology, and common comparison traps.
Latency vs Throughput: Setting Realistic Targets for AI Inference
How to separate latency from throughput when benchmarking AI inference, set percentile targets, and size hardware for the load you will actually serve.
Troubleshooting Low Accelerator Utilization: A Step-by-Step Approach
A practical order of checks for diagnosing why an AI accelerator is underused, from profiling basics to batch size, kernels, and synchronization.
How to Choose an AI Accelerator for Your Workload
A practical selection guide for engineers and technical buyers choosing an AI accelerator, starting from workload, memory, software and operations.
Systolic Arrays and Tensor Cores: What the Matrix Hardware Actually Does
A plain explanation of matrix engines in AI chips, how systolic arrays and tensor cores differ, and why tile shapes decide how fast your model runs.