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.