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NEW Physical-AI Data Stack

Full solution for the
Physical-AI Data Stack.

Autonomous agents that scan, clean, and eliminate wasted infrastructure spend for world-model builders and training-data providers — across GPU, storage, and tokens.

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Where it sits

Every world-model builder and data provider is GPU- and storage-bound. Parsimo's agents run 24/7 to turn that cost center into margin — the same Scan · Clean · Save engine, tuned for the physical-AI stack.

Three layers · one autonomous agent

Waste lives in three places.
Parsimo cleans all of them.

From cross-cloud infrastructure to GPU utilization to petabyte-scale data — one agent, scanning and cleaning continuously.

🗂
01

Infrastructure

Cloud + token cost saving
  • Cross-cloud / neocloud / on-prem spend control
  • Agent-driven right-sizing & idle elimination
  • LLM token & inference cost reduction
40+
optimization dimensions, scanned live · multi-cloud, one control plane
⚙
02

GPU Workload

Utilization efficiency
  • Continuous GPU / LLM workload tuning
  • Bin-packing & idle-GPU reclamation
  • Throughput maximized per accelerator
  • No human-in-the-loop required
40% → 90%
GPU utilization lift
🗄
03

Data

Storage lifecycle
  • Deduplication across training pipelines
  • Compression of multimodal sensor data
  • Hot / cold tiered data management
  • Storage optimized as datasets scale to PB
Hot ↔ Cold
tiered automatically
Why it matters

The cost center of the physical-AI era.

World models train on petabytes of multimodal data on the most expensive accelerators on earth. Left unmanaged, that's where the margin goes.

~30%
of GPU & storage spend is waste left unmanaged
2.3×
throughput from the same fleet after utilization tuning
24/7
autonomous scan & clean — no engineers in the loop

Turn your cost center into margin.

Connect in under a minute. Parsimo scans your physical-AI stack, finds the waste, and cleans it — continuously.

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