NVIDIA A100 (40/80 GB) and H100 accelerators with dedicated VRAM, single-tenant isolation and flexible 1–8 GPU per node configurations.
High-capacity AI hosting
GPU infrastructure built for production AI.
Deploy NVIDIA A100 and H100 instances for inference, training and RAG pipelines. Private networking, NVMe storage and managed Kubernetes included.
Sub-100msTime-to-first-token on optimized serving stacks
100 GbpsNode-to-node InfiniBand interconnect
NVMe SSD for hot data, S3-compatible object storage for checkpoints and datasets, plus persistent volumes for model artifacts and logs.
Private VLANs between GPU nodes, 100 Gbps InfiniBand for multi-node training, and low-latency egress to your application layer.
Prometheus monitoring, Grafana dashboards, managed Kubernetes orchestration, and dedicated SRE support for production deployments.
Low-latency model serving
Run LLMs, embedding models and vision APIs behind auto-scaling endpoints. Consistent sub-second response times at thousands of concurrent requests.
Training environments
LoRA adapters, full fine-tunes, RLHF pipelines and evaluation harnesses on dedicated GPU clusters with checkpoint persistence and experiment tracking.
Private AI cloud
Reserved multi-node clusters with dedicated networking, SOC 2 compliance, custom firewall rules and a named account engineer for architecture planning.
Sizing helper
Estimate your AI infrastructure needs
Answer a few questions about your workload — model size, request volume, latency requirements and training schedule — and get a recommended starting configuration with estimated monthly cost.
From prototype to production
Start with a single GPU for development, validate performance under load, then scale to a reserved multi-node cluster with private networking and dedicated support.
Compute, storage and support
Each estimate includes recommended GPU type and count, expected monthly cost range, suggested NVMe and object storage allocation, and add-ons like monitoring and managed Kubernetes.