GPUOPs was born out of frustration — spreadsheets and ad-hoc scripts weren't cutting it for tracking ML benchmark regressions. We built the tool we always wanted.
To give every ML engineering team — from solo researchers to large enterprise labs — the same visibility into GPU performance that top AI companies have built internally. Faster debugging, smarter optimization, fewer production surprises.
Every decision we make is guided by measurable impact on your GPU utilization and throughput.
Built by ML engineers, for ML engineers. We care deeply about the workflows that actually matter.
Shared dashboards, audit logs, and leaderboards keep every team member aligned and accountable.
A complete platform for measuring, optimizing, and governing your GPU workloads — from the first benchmark to production cost control.
Log throughput, latency (P50/P99), VRAM usage, power draw, and energy efficiency for every run. Searchable history, retained forever.
Instant, AI-powered recommendations after each run — precision changes, batch tuning, memory optimization, and more.
Rule-based alerts fire when VRAM exceeds thresholds, latency spikes, or occupancy drops — catch regressions before they reach production.
Compare up to 4 runs side-by-side with delta analysis. Sweep batch sizes and sequence lengths to map the efficiency frontier live.
Spin up RTX 4090, A100, H100, or H200 environments billed per second from your wallet — stop an instance and billing stops too.
Unified hard budgets across self-hosted GPU infrastructure and LLM API spend, with per-customer attribution and margin tracking.
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