Hammerhead provides a SaaS platform that uses reinforcement‑learning agents to continuously orchestrate power, cooling, and GPU scheduling in AI data centers, redirecting unused “stranded” megawatts to AI workloads in real time. By unlocking up to 30 % more compute capacity within existing power limits, the solution boosts token throughput, reduces marginal costs, and creates new revenue streams for colocation providers, AI cloud operators, and enterprise AI factories.
Funding
Funding not disclosed
Founders
Product
Problem
Data centers that host AI workloads typically operate at only 30‑50 % of their provisioned power capacity, leaving large amounts of “stranded” megawatts idle while GPU demand outpaces available electricity. This power bottleneck limits token throughput, slows AI deployment, and prevents operators from monetizing existing infrastructure.
Solution
Hammerhead’s ORCA (Orchestrated Reinforcement‑Learning Control Agents) platform continuously monitors power, cooling, and compute resources inside a data center and dynamically reallocates unused headroom to AI workloads. By applying reinforcement‑learning agents to balance workload placement, GPU operating points, and cooling system loads, ORCA can increase token throughput by up to 30 % without exceeding the facility’s power budget. The software runs on existing hardware, requires no additional grid connection, and delivers real‑time decisions that keep end‑user performance unchanged while unlocking new revenue from previously idle power. Operators gain a cloud‑style dashboard that shows unlocked megawatts, projected AI token output, and financial impact, enabling faster AI scaling and higher gross margins.
Target Audience
Primary customers are AI‑focused data‑center operators, colocation providers, and hyperscale cloud or enterprise AI factories that need to maximize GPU utilization within fixed power limits.
Features
- Real‑time reinforcement‑learning agents that orchestrate power, cooling, and GPU scheduling across the entire data‑center stack
- Automatic detection of unused “headroom” power and safe redirection to AI workloads while respecting operator‑defined power guardrails
- Cross‑layer optimization that adjusts GPU frequency, batch size, and cooling setpoints to maximize tokens per joule
- Integrated dashboard with live metrics on unlocked megawatts, token throughput, and revenue projections
- API and OEM‑ready interfaces for seamless integration with existing DCIM, BMS, and AI orchestration tools
- Security‑focused design meeting enterprise cyber‑risk and reliability standards