GPU Infrastructure

Enterprise-Scale GPU Clusters on Demand

Access 10,000+ H100 and A100 GPUs with 99.9% uptime and elastic scaling. Train, fine-tune, and serve models without managing hardware.

Get Started Today

Ready to transform your business with cutting-edge AI? Contact our team to learn more about this service and get started.

Key Features

Our GPU Infrastructure provides bare-metal performance with cloud convenience. Reserve clusters for training runs or scale inference endpoints on demand — all managed through a single dashboard.

H100 and A100 clusters — latest-generation NVIDIA hardware with NVLink and InfiniBand interconnects

10,000+ GPUs available across multiple regions for low-latency access worldwide

99.9% uptime SLA backed by redundant power, cooling, and network infrastructure

Elastic scaling — spin up from 1 to 1,000+ GPUs in minutes, scale down when done

Use Cases

GPU Infrastructure powers the most demanding AI workloads:

Large-scale model training — Train foundation models on multi-node clusters with distributed data parallelism

Fine-tuning and RLHF — Customize open-source models on your proprietary data with efficient GPU utilization

Inference serving — Deploy models behind auto-scaling endpoints with latency guarantees

Batch processing — Run large-scale embedding generation, dataset preprocessing, or evaluation benchmarks

Research experimentation — Rapid prototyping with flexible GPU allocations and preemptible instances

Technical Architecture

Purpose-built infrastructure for AI workloads:

Cluster Orchestrator — Kubernetes-based scheduling with GPU-aware bin packing and topology-aware placement

Storage Layer — High-throughput parallel file system (200 GB/s aggregate bandwidth) with NFS and S3-compatible interfaces

Network Fabric — 400 Gbps InfiniBand between nodes, RDMA support for NCCL-optimized distributed training

Container Runtime — Pre-built images with PyTorch, JAX, TensorFlow, and vLLM; custom images supported via Docker

Monitoring — GPU utilization, memory, temperature, and job progress visible in real-time dashboards

Getting Started

Launch your first GPU job in minutes:

Create an account and set your billing method

Choose your hardware — select GPU type, count, and region from the dashboard

Launch an environment — start a Jupyter notebook, SSH session, or submit a batch job

Upload your data — use the CLI, web upload, or mount from S3/GCS

Monitor and scale — track utilization in real-time and adjust resources as needed