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Data lake, data warehouse and lakehouse are often used loosely. Here is a clear guide to what each one is, how they differ and why AI...
Object storage is the quiet foundation under photo apps, backups and AI datasets. This explainer covers how it differs from file and block storage and why...
Data centers store information on several kinds of media, each with its own balance of speed, capacity and cost. Here is how the tiers fit together.
Data processing units take over networking, storage and security chores from a server's main processor. Here is what they are, why clouds use them and how...
Every server reaches the network through a network interface card. Here is what NICs do, how SmartNICs differ and why AI servers carry several high-speed cards.
Latency is the delay between asking and getting an answer. Here is where it comes from, how it is measured and why it shapes where data...
When thousands of chips work together for weeks, some hardware will fail along the way. Here is how AI clusters are designed to keep going when...
AI hardware coverage is full of jargon. This glossary defines common terms in plain English, from accelerator and HBM to tape-out and yield.
Researchers are exploring computing approaches very different from today's digital accelerators. Here is a general look at neuromorphic, analog and optical computing ideas.
Quantization is not the only way to make AI models leaner. Here is how pruning, sparsity and distillation work, and how hardware is adapting to them.
Leading-edge chips are made on silicon wafers 300 millimeters across. Here is why wafer size matters, how it grew and why moving larger has proved so...
Benchmarks are how the industry compares AI hardware on real workloads. Here is how they work, why they are useful and what they cannot tell you.
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