H100 SXM5 on AWS
Visit AWSAWS delivers the NVIDIA H100 SXM5 GPU through its EC2 P5 instances, such as the p5.48xlarge with 8 H100 GPUs, each boasting 80GB HBM3 VRAM on the Hopper architecture. This combination stands out for powering the most demanding AI training and inference workloads, including large language models and HPC simulations, at unprecedented scale. AWS's global infrastructure, with Availability Zones across regions, ensures low-latency, high-availability deployments. Deep integration with SageMaker provides fully managed ML workflows, from data preparation to model deployment, streamlining development for enterprises. Key value propositions include per-second billing, Spot Instances for up to 90% cost savings, and Elastic Fabric Adapter (EFA) networking up to 3,200 Gbps for multi-node scaling. Ideal for large-scale enterprises and research organizations needing robust ecosystem integration, reliable performance, and cost efficiency without managing underlying hardware.
Why NVIDIA H100 SXM5 on AWS?
Choose AWS for NVIDIA H100 SXM5 due to its unmatched ecosystem integration, including seamless SageMaker compatibility for end-to-end ML pipelines and proprietary Trainium/Inferentia for cost-optimized inference. P5 instances leverage NVLink for 900 GB/s GPU-to-GPU bandwidth, enabling efficient multi-GPU training. AWS's per-second billing and Spot Instances minimize costs for variable workloads, while global redundancy across 30+ AZs supports mission-critical applications. High-speed EFA and FSx for Lustre storage complement the H100's capabilities for exascale AI, offering better scalability than competitors for distributed training. This setup excels for teams prioritizing managed services, reliability, and integration over raw hardware access.
Live Pricing
Real-time NVIDIA H100 SXM5 offers from AWS
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 AWS | 8×NVIDIA H100 SXM5 80GB VRAM | 80GB | 192 vCPU 2048GB RAM | Virginia | $12.29/GPU/hr $98.32/hr total (8×) | |||
AWS | 8×NVIDIA H100 SXM5 80GB VRAM | 80GB | 192 vCPU 2048GB RAM | Oregon | $12.29/GPU/hr $98.32/hr total (8×) |
Performance Notes
On AWS P5 instances, H100 SXM5 GPUs deliver peak FP8 performance up to 4 petaFLOPS per GPU, with excellent scaling via NVLink (900 GB/s bidirectional) across 8 GPUs per node. Inter-node communication uses EFA at 3,200 Gbps, supporting efficient multi-node training for models like GPT-4 scale. Benchmarks show 2-3x faster LLM training vs. A100s, with strong utilization in frameworks like PyTorch and TensorFlow. Storage via FSx Lustre provides 100+ GB/s throughput. Actual performance varies by workload, software stack, and optimization; AWS publishes some MLPerf results, but user-specific benchmarks recommended. No public data on PCIe variants, as SXM5 is NVLink-optimized.
The dominant force in global cloud computing with deep integration of GPUs into its ecosystem for machine learning and other services.
Best For
Unique Features
- Proprietary silicon like Trainium and Inferentia chips
- Fully managed ML development environment with SageMaker
VRAM
80GB
Architecture
Hopper
Tier
enterprise
Platform Features
Getting Started
Launching NVIDIA H100 SXM5 on AWS is straightforward via EC2 P5 instances. Use the console, CLI, or SageMaker for managed access. Requires an AWS account with sufficient quotas; request P5 limits if needed. Deep Learning AMIs pre-install CUDA 12.x, cuDNN, and ML frameworks for immediate productivity.
Steps
- 1Log into AWS Console, navigate to EC2, and select 'Launch Instance.'
- 2Choose 'Deep Learning AMI GPU PyTorch' or similar, then select p5.48xlarge instance type.
- 3Configure vCPU (192), RAM (2TB), and 8x H100 GPUs; add EBS or FSx storage.
- 4Set up security groups for SSH/Jupyter, launch with Spot for savings or On-Demand.
- 5SSH in, verify GPUs with 'nvidia-smi', and start training scripts.
Pro Tips
- Request P5 quota increases early; use Spot Instances with fallback to On-Demand for 70-90% savings on interruptible jobs.
- Integrate with SageMaker for managed training jobs, auto-scaling clusters, and HyperPod for thousands of H100s.
- Optimize with NCCL for collective ops and EFA for multi-node; monitor via CloudWatch for GPU utilization.
Frequently Asked Questions
What is AWS's billing model for NVIDIA H100 SXM5?▾
AWS bills per-second for GPU instances including NVIDIA H100 SXM5. Per-second billing ensures you only pay for exactly the compute time you use, which is particularly cost-effective for short experiments, iterative development, and workloads with variable duration.
Does AWS offer spot instances for NVIDIA H100 SXM5?▾
Yes, AWS offers spot/preemptible instances for NVIDIA H100 SXM5, which can reduce costs by 50-80% compared to on-demand pricing. Spot instances are ideal for fault-tolerant workloads like batch inference, hyperparameter tuning, and training jobs with checkpointing. Note that spot instances can be interrupted when demand is high, so ensure your workflow can handle preemption gracefully.
Does AWS offer reserved instances for NVIDIA H100 SXM5?▾
Yes, AWS offers reserved instance pricing for NVIDIA H100 SXM5, which can provide significant discounts (typically 20-40% off on-demand rates) for committed usage periods. Reserved instances are ideal for predictable, long-running workloads like production inference services, ongoing training pipelines, or development environments that run continuously. Contact AWS for current reserved pricing and commitment terms.