AI Systems Performance Overview
AI systems performance work starts with a clear workload, a measurement target, and a bottleneck hypothesis.
Top-down model
Begin with end-to-end latency or throughput, then break the workload into compute, memory, communication, and scheduling components.
Inline math example: latency can be treated as $T = T_\text{compute} + T_\text{memory} + T_\text{overhead}$ for a first-pass model.
Display math example:
\[\text{throughput} = \frac{\text{tokens processed}}{\text{elapsed seconds}}\]Measurement notes
- Define the exact input shape and batch size.
- Record warmup and steady-state behavior separately.
- Keep benchmark scripts reproducible and public-safe.