Juice is an AI tool that allows GPUs to be fully network attached, providing bare-metal performance on both graphical and ML tasks over standard networking.

Key Features:

Scale up and down development with no setup time or commitment to the underlying machine or stack.

Take advantage of GPU's load-balancing telemetry and make GPUs sharable across multiple clients and tasks.

Unlock the unused capacity of GPUs, increasing GPU utilization and allowing organizations to do more with less.

Access any GPU over standard network infrastructure with a simple software install, eliminating the need for new hardware.

Free trapped capacity by leveraging under-utilized or unused GPUs in workstations, cloud provisioning, and servers.

Use Cases:

Training machine learning models on multiple GPUs simultaneously.

Running high-performance graphical applications over a network.

Sharing GPU resources across different teams or departments.

Benefits:

Increased flexibility in GPU resource allocation.

Improved GPU utilization and efficiency.

Cost savings by maximizing existing GPU resources.