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Enterprise Layer Intelligence
Categories/AI Hosting & Inference/CloudRift
CloudRift

CloudRift

Founded 2024

Manage and virtualize private GPU clusters for enterprise model training and inference

You can use CloudRift to manage GPU clusters, virtual machines, and containers inside private data centers or on-premise environments. It provides a central control plane to track hardware health, temperature, power draw, and memory across NVIDIA and AMD chips. Datacenter operators can create multi-tenant environments, configure billing tiers, and assign isolated compute resources to different teams. Developers can also deploy open-weight language models with OpenAI-compatible endpoints, access pre-configured machine learning environments, and provision GPU compute by the hour or pay per token.

What CloudRift does

Deploy language model inference endpoints on private serversAllocate virtual machines and containers across GPU nodesMonitor GPU temperature and memory usage in real timeAssign credit limits and track usage costs across multiple teamsDrain and schedule server nodes for hardware maintenanceAttach persistent storage volumes to computing instancesSwitch model API requests using OpenAI-compatible endpointsUnified dashboard for managing bare-metal servers, virtual machines, and containersReal-time hardware telemetry tracking temperature, power draw, clock speed, and memoryDirect Grafana integration for detailed GPU utilization analyticsHardware-neutral architecture supporting both NVIDIA and AMD ROCm graphics processorsOpenAI-compatible inference endpoints for serving open-weight modelsMulti-tenant isolation with custom billing rules, credit caps, and user managementPre-configured runtime environments for frameworks like PyTorch and vLLMWhite-label customer portal options for data center operators

Frequently asked

GrafanaGrafanaPyTorchPyTorch

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CloudRift, AI hosting, GPU orchestration, GPU virtualization, LLM inference, on-premise AI, sovereign AI, NVIDIA GPUs, AMD ROCm, bare metal compute, cluster management, model serving, private cloud, datacenter management, vLLM