Term tag: Performance

Distributed Computing

Distributed computing splits work across separate machines that talk only by messages. Why partial failure, not speed, is the defining problem.

Distributed Training

How one model is trained across thousands of GPUs — data, tensor and pipeline parallelism, and why the network rather than the chip becomes the bottleneck.

FLOPs (Floating Point Operations)

The unit AI compute is measured in — how a training run's FLOP budget is counted, why FLOP and FLOPS are not the same, and why regulators use the number.

Inference

Running a trained model to get an answer. Training is paid once; inference is paid on every request — and it is where most AI money goes.

Performance

AI performance is not one number but a frontier of quality, speed, and cost—improving one axis (via quantization or batching) usually trades off another.

State of the Art Model (SOTA)

There is no single SOTA model. How to read a benchmark claim — and why a 3-point lead on GPQA Diamond is six questions, well inside the noise.

Vectorization

Replacing element-by-element loops with operations on whole arrays at once, so the work runs as SIMD instructions on a CPU or in parallel on a GPU.