From single-GPU inference to multi-thousand node training clusters. Every component purpose-built for maximum throughput and minimal latency.
Access the latest NVIDIA GPUs with bare-metal performance and cloud flexibility.
80GB HBM3 memory, 3.35 TB/s bandwidth. The gold standard for large-scale training with 4th-gen Tensor Cores and Transformer Engine.
141GB HBM3e memory with 4.8 TB/s bandwidth. Next-gen memory capacity for the largest models without model parallelism overhead.
Blackwell architecture with 192GB HBM3e per GPU. 72-GPU NVLink domains for unprecedented all-reduce performance.
| Specification | H100 SXM5 | H200 SXM | GB200 NVL72 |
|---|---|---|---|
| GPU Memory | 80GB HBM3 | 141GB HBM3e | 192GB HBM3e |
| Memory Bandwidth | 3.35 TB/s | 4.8 TB/s | 8 TB/s |
| FP8 Performance | 3,958 TFLOPS | 3,958 TFLOPS | 10,000+ TFLOPS |
| Interconnect | NVLink 4.0 | NVLink 4.0 | NVLink 5.0 |
| Max Cluster Size | 16,384 GPUs | 8,192 GPUs | 4,608 GPUs |
| Availability | All 12 regions | 8 regions | 3 regions (expanding) |
400Gbps InfiniBand fabric with non-blocking fat-tree topology. Zero bottlenecks at any scale.
Every GPU node connected via 400Gbps InfiniBand HDR with SHARP in-network computing for collective operations.
Full bisection bandwidth topology ensures consistent performance regardless of communication pattern or cluster size.
RoCEv2 support for workloads that need Ethernet compatibility with near-InfiniBand latency (sub-2Ξs).
Dedicated network segments with hardware-enforced isolation. No noisy neighbors, no shared fabric contention.
Private fiber backbone connecting all 12 regions with sub-10ms inter-region latency and 100Tbps aggregate capacity.
Real-time congestion monitoring, per-flow analytics, and adaptive routing for optimal all-reduce performance.
High-throughput parallel filesystem delivering 2TB/s aggregate bandwidth. Your data, always hot.
Lustre-based distributed filesystem optimized for AI workloads. Handles millions of small files and multi-TB checkpoints with equal efficiency.
Automatic data lifecycle management moves data between NVMe, SSD, and object storage based on access patterns and policies.
Enterprise-grade durability with cross-region replication, point-in-time snapshots, and immutable backup policies.
GPU-native Kubernetes with first-class support for distributed training, batch scheduling, and auto-scaling.
Topology-aware scheduler places pods on GPU nodes with optimal NVLink and InfiniBand locality for maximum collective performance.
MPI Operator, PyTorch Elastic, and custom training operators for one-click distributed training job deployment.
All-or-nothing scheduling ensures distributed training jobs get all required GPUs simultaneously. No partial allocations.
Scale from 0 to thousands of GPU nodes based on pending workloads. Supports spot instance integration for cost optimization.
Namespace-level GPU quotas, priority classes, and fair-share scheduling for teams sharing cluster resources.
Fully managed, highly available control plane with automatic upgrades, etcd backups, and 99.95% API server SLA.
Deploy models to production with optimized serving infrastructure. Sub-50ms p99 latency at any scale.
TensorRT-LLM and vLLM backends with continuous batching, PagedAttention, and speculative decoding for maximum tokens/second.
Deploy inference endpoints across 12 regions with intelligent routing. Requests automatically served from the nearest healthy replica.
Real-time metrics on latency, throughput, token usage, and model quality. Built-in A/B testing and canary deployments.
Start with $500 in free credits. No commitment, no credit card required.