Juice Labs
Open siteCoding & Development
Introduction
Juice Labs provides affordable and easily-accessed GPU resources through their GPU-over-IP software.
Juice Labs Product Information
Juice - GPU over IP is a software platform that enables GPU over IP, turning GPUs into remote virtual resources that can be pooled, shared, and accessed by AI and graphics workloads without modifying existing code. It provides ephemeral compute across networks, allowing GPUs to be attached instantly where needed, from workstation to edge to cloud, with dynamic allocation and seamless remote access.
How it works
- Install the Juice software to enable remote GPU access.
- Pool and share GPUs across multiple application hosts over standard networks.
- Dynamically allocate GPU capacity on demand to meet workload needs.
- Access GPUs from anywhere as if they were local, with no changes to applications or hardware.
Use Cases
- Turn any CPU-only node into a GPU node, on the fly, even across clouds.
- Route GPU capacity from areas of abundance to areas of scarcity within an organization.
- Enable large-scale AI workloads with distributed, private GPU networks.
- Support dynamic scheduling for high-performance computing and data science teams.
How It Works at a Glance
- Network-attached, remote, virtual, pooled, and shared GPUs.
- Instant GPU attachment when needed; quickly burst to more powerful GPUs and release just as fast.
- Works across Endpoint, Edge, and Cloud environments.
- Any system can connect to a remote GPU and use it as if it were local.
Capabilities
- GPU flexibility from workstation to edge to cloud
- Dynamic sharing and pooling of GPU resources
- Access any GPU capacity on any network
- No code changes required for adoption
- Real-time, on-demand GPU acceleration across the network
What customers say
- Widely adopted by enterprises and universities for private AI, scalable GPU scheduling, and cross-network GPU utilization.
How to Try / Get More Info
- Contact Sales for a demonstration or free trial.
- Explore documentation and product blogs for deeper technical details.
Core Features
- GPU-over-IP: remote, virtual, pooled, and shared GPUs accessible over standard networks
- Ephemeral Compute: on-the-fly GPU allocation and reallocation without code changes
- Dynamic GPU pooling: share GPUs across hosts and balance load automatically
- Global reach: attach GPUs from anywhere (workstation, edge, cloud)
- Seamless integration: no modifications required to existing applications or hardware
- Flexible deployment: suitable for data centers, labs, and enterprise environments
- High utilization: keep GPUs near 100% utilization by flowing compute where needed