Operators, product owners, and people affected by the gpu training decision.
Training systems · weeks 15
GPU-aware training
Training repeatedly OOMs and misses its nightly completion window.
Opening production brief
A measurable problem, before a model.
kernels, HBM bandwidth, arithmetic intensity, pinned memory with version, owner, event time, and availability contracts.
Training repeatedly OOMs and misses its nightly completion window. Every solution must state latency, cost, capacity, and fallback limits.
Offline evidence plus a deployable resolution: A profiled, mixed-precision, memory-budgeted training loop.
Frame and baseline
Turn the production problem into explicit data, metric, and baseline contracts.
- 01↗
What the GPU actually executes
GPU performance comes from feeding a few substantial, well-shaped kernels—not merely from placing tensors on a CUDA device.
65–90 min · checkpoint · deterministic lab - 02↗
Keep it fed: loaders and async copies
A fast training step needs an equally deliberate supply chain from storage to pinned memory to the device.
65–90 min · checkpoint · deterministic lab
Build and stress
Formalize the machinery, test counterexamples, and expose system limits.
- 03↗
Mixed precision without NaNs
Mixed precision is trustworthy only when dtype choices, loss scaling, skipped steps, and quality parity are all observable.
65–90 min · checkpoint · deterministic lab - 04↗
Activations, accumulation, and checkpointing
Checkpointing trades compute for activation memory; accumulation trades update frequency for batch memory—measure and validate each trade separately.
65–90 min · checkpoint · deterministic lab
Resolve and operate
Join model behavior to architecture, observability, rollout, and rollback.
- 05↗
Profile from timeline to cost per sample
Profile a controlled workload, explain the timeline, and judge improvements by cost to an unchanged quality target.
65–90 min · checkpoint · deterministic lab - HW↗
Single-GPU optimization plan
Profile and optimize a correctness-locked trainer without sacrificing model quality.
Rubric · staged hints · reference resolution
Prerequisite guidance
1 concepts make this faster.
Prerequisites are guidance, never hard gates.