AI-chips en hardware

CPUs, GPUs, and specialized accelerators have different strengths in computation, memory, connectivity, and software support. A peak arithmetic specification does not by itself predict application performance.

Key takeaways

• Match hardware to the workload.
• Evaluate memory and software support.
• Compare measured application performance rather than peak specifications alone.

Start with the workload. Training, short interactive inference, large-batch inference, and on-device processing can stress different resources. Matrix arithmetic may be important, but moving weights and intermediate data can also dominate the time or energy required. Check memory capacity and bandwidth alongside compute. The model must fit with working buffers, cached state, and concurrent requests. Multi-device execution adds communication costs and software complexity, so aggregate memory is not automatically equivalent to one simple pool. Numerical formats affect both speed and representation. Lower precision can reduce storage and enable faster operations on compatible hardware, but models and tasks need evaluation for accuracy changes. Hardware support, kernels, and the execution framework determine whether an advertised capability is actually used. Compare systems using reproducible workloads with stated batch sizes, input lengths, precision, and software versions. Measure latency, throughput, power, and cost per useful task. A vendor demonstration can inform investigation, but a purchase or deployment decision needs evidence for the intended application.

1. Construct a model with one billion parameters stored at 16 bits each.
2. The weights alone occupy roughly two billion bytes, or 2 GB in decimal units. This excludes activations, caches, runtime buffers, and framework overhead.
3. Use the estimate as a starting point, then measure actual memory for the intended serving configuration.

The arithmetic gives a weight-storage estimate, not a complete hardware requirement or performance claim.