AWS vs Cirrascale

AWS employs per-second on-demand billing for EC2 GPU instances (e.g., p5.48xlarge with 8x H100 at ~$98/hour), spot instances offering 50-90% discounts for interruptible workloads, and reserved/savings plans for 1-3 year commitments yielding up to 72% savings. This granular model suits bursty or variable usage but introduces complexity with egress fees (~$0.09/GB) and minimum instance sizing. Cirrascale uses fixed monthly billing for bare-metal servers (e.g., 8x H100 configurations starting ~$20,000/month), eliminating per-hour granularity and spot options. This favors sustained, high-utilization runs (>80% uptime) but penalizes short-term or experimental use, as contracts enforce full-month commitments without refunds for downtime.

AWS provides superior value for small experiments and fine-tuning via spot instances, potentially reducing costs by 70% for <1-week jobs, and production inference with auto-scaling. Cirrascale offers better value for large training runs (e.g., weeks-long LLM pretraining), where bare-metal yields 10-20% higher effective throughput per dollar due to no virtualization tax and consistent performance, assuming >3-month commitments. For batch inference, AWS edges out with serverless options like SageMaker Batch Transform. Real-time inference favors AWS's global low-latency edge. Overall, AWS wins for flexibility (<50% utilization); Cirrascale for steady-state HPC (>70% utilization).

AWS relies on virtualized EC2 instances with Elastic Fabric Adapter (EFA) for multi-GPU scaling, EBS/GP3 storage (up to 16TB NVMe), and full Kubernetes support via EKS. Global regions/AZs provide redundancy; networking hits 400Gbps. Cirrascale deploys non-virtualized bare-metal racks with direct GPU-to-GPU NVLink/InfiniBand (up to 800Gbps), local NVMe storage, and Kubernetes compatibility. No hyperscale redundancy, focusing on single-site high-density clusters.

AWS delivers reliable multi-GPU scaling (e.g., 100s of H100s via Trainium), but virtualization incurs 5-10% overhead; spot preemptions disrupt long jobs. GPU availability is high but queues during peaks. Cirrascale achieves near-peak bare-metal performance (e.g., 99% H100 utilization in multi-node), superior for DGX-like scaling in training; diverse accelerators (MI300X, Grace) enable specialized workloads. Limited public benchmarks suggest 15-25% faster wall-clock times for Cirrascale in sustained DL jobs.