Juice Labs provides a remote GPU pooling and sharing platform that allows any application to access GPU resources over standard networking without requiring changes to existing hardware. This technology enables efficient scaling and dynamic allocation of GPU resources, addressing the challenges of underutilization and accessibility in high-performance computing environments.
Funding
Funding not disclosed
Founders
Product
Problem
In high-performance computing environments, GPUs are often underutilized due to static allocation and the inability to efficiently share resources across multiple applications or users. Traditional methods lack the flexibility to dynamically adjust GPU resources based on workload demands, leading to wasted capacity and increased costs. This inflexibility hinders efficient scaling and resource balancing, especially across diverse computing environments like data centers and cloud deployments.
Solution
Juice Labs offers a remote GPU pooling and sharing platform that enables any application to access GPU resources over standard IP networks. The software allows for dynamic allocation of GPU resources, enabling near-100% utilization by sharing GPUs with multiple application hosts remotely. This solution eliminates the need for changes to existing hardware or applications, providing a seamless way to scale GPU resources independently from CPUs, even across different data centers. By virtualizing GPU access, Juice Labs facilitates efficient resource balancing and maximizes performance for AI and graphics workloads.
Target Audience
The primary target audience includes enterprises, Fortune 100 companies, universities, and digital infrastructure providers that require efficient GPU resource management for AI, graphics, and high-performance computing workloads.
Features
- GPU-over-IP technology enabling remote access to GPUs over standard networks
- Dynamic GPU sharing and pooling for near-100% utilization
- Compatibility with existing applications and hardware without requiring modifications
- Support for GPU orchestration without Kubernetes
- Ability to turn any CPU-only node into a GPU node on-the-fly, even across cloud environments
- Flexible virtualization for sharing a single GPU across multiple systems or aggregating multiple GPUs into virtual clusters
- Instant GPU acceleration that can be dynamically adjusted based on workload demands