Key Technologies
Last updated
Last updated
Uses reinforcement learning to optimize GPU resource distribution in real-time.
Increases GPU efficiency by up to 170% by prioritizing workloads dynamically.
Balances energy consumption vs. performance, ensuring GPUs are always running at peak efficiency.
Reduces wasted compute power and energy costs.
Ensures time-sensitive AI tasks get priority access.
Splits a single GPU into multiple, isolated environments using Docker/Kubernetes.
Allows multiple AI jobs to run simultaneously without interference.
Ensures optimal GPU utilization even during peak demand periods.
Enables seamless multi-tasking on decentralized hardware.
AI companies no lo
nger need dedicated GPUs, they can rent fractional power as needed.