Neuralwatt offers a power‑management platform that captures and reallocates stranded electrical capacity in data centers to active GPU workloads. By dynamically adjusting power draw based on real‑time grid demand, it enables higher GPU density per rack, boosting AI inference throughput and revenue without expanding power infrastructure, while also lowering cooling needs and carbon intensity.
Funding
Funding not disclosed
Founders
Product
Problem
Data centers often have unused or “stranded” electrical capacity because power infrastructure is provisioned for peak loads that rarely occur. This underutilized power limits the number of GPUs that can be deployed, reducing AI inference throughput and revenue per rack.
Solution
Neuralwatt provides a power‑management platform that captures and reallocates stranded electrical capacity to active GPU workloads. By dynamically adjusting power draw based on real‑time grid demand, the system enables higher GPU density within existing racks, increasing inference throughput without expanding the power infrastructure. The platform also offers flexible consumption modes that lower cooling requirements and reduce carbon intensity, extending hardware lifespan while matching workload scaling to available power.
Target Audience
Primary customers are data‑center operators and AI inference service providers seeking to maximize GPU utilization and revenue within existing power and space constraints.
Features
- Real‑time monitoring of available power capacity and automatic redistribution to GPU clusters
- Dynamic power throttling that aligns GPU consumption with grid demand and cooling constraints
- Software layer that enables higher GPU density per rack without additional power hardware
- Integrated analytics to optimize inference workload placement for maximum kilowatt efficiency
- Compatibility with standard data‑center power management interfaces and GPU orchestration tools