deploybase

Cheapest H100 in US East: Provider Pricing Ranked

Deploybase · August 26, 2025 · GPU Pricing

Contents

Cheapest H100 Us East: H100 Pricing in US East

Cheapest H100 Us East is the focus of this guide. H100 standard for big training. US East: $2.69-$3.78/hr depending on provider.

RunPod: $1.99/hr PCIe (lowest), $2.69/hr SXM. Per-minute billing for short runs. Lambda Labs: $2.86/hr. Slightly pricier than RunPod SXM, but consistent. 48-hr run costs ~$42 more than RunPod PCIe. CoreWeave: 8x H100s = $6.16/GPU. More expensive per GPU, better for clusters.

CoreWeave bundles eight H100s at $49.24 per hour, breaking down to $6.16 per GPU. This bulk pricing makes CoreWeave more expensive for single-GPU needs but cost-effective for multi-GPU training clusters. Teams deploying multiple models simultaneously benefit from CoreWeave's configurations.

AWS EC2 H100 instances cost between $3.06 and $3.98 per hour depending on instance configuration. Markup reflects AWS's premium for managed infrastructure and ecosystem integration. Reserved instances reduce costs 30-40 percent for long-term commitments.

Google Cloud H100 access remains limited but competes with AWS on pricing. Pricing typically ranges $3.00-3.50 per hour. Commitment discounts apply similarly to AWS. Regional availability varies; US East access is not guaranteed across all time periods.

Provider Rankings

Cheapest Option: RunPod at $1.99 per H100 PCIe hour represents the lowest fixed hourly rate. Per-minute billing enables cost precision. 48-hour training session costs approximately $95. No commitment required, offering maximum flexibility.

Mid-Tier Option: RunPod at $2.69 per H100 SXM hour offers higher memory bandwidth. 48-hour training session costs approximately $129.

Budget-Friendly Alternative: Lambda Labs at $2.86 per H100 PCIe hour costs slightly more than RunPod but provides stable infrastructure. Customer support from Lambda exceeds RunPod's community-focused model.

production Option: AWS EC2 at $3.06-3.98 per hour includes SLA guarantees and professional support. Reserved instances reduce costs to $2.14-2.79 per hour with one-year commitment. Integration with AWS services provides ecosystem benefits justifying premium costs.

Bulk Deployment Option: CoreWeave at $6.16 per GPU for eight-unit bundles suits large deployments. Per-GPU cost drops below single-unit pricing when deploying multiple models. Teams using fewer GPUs find this option uneconomical.

Spot and Preemptible Options: AWS spot instances cost 60-75 percent less than on-demand pricing, roughly $0.92-1.50 per hour. Instances can terminate without warning. Batch workloads with checkpointing use dramatic cost savings. Interactive workloads require standard instances.

Hidden Costs Beyond Hourly Rates

Storage costs accumulate beyond compute charges. EBS volumes cost $0.10-0.125 per GB-month for General Purpose SSD. Large model checkpoints of 100GB+ accumulate $10-15 monthly storage costs. Persistent storage between training sessions adds expenses.

Data transfer costs emerge when moving datasets. Uploading 100GB of training data to RunPod costs $0 (internal transfers). AWS charges $0.02 per GB for egress beyond free tiers, costing $2 per 100GB. Data residency planning reduces egress charges.

Network throughput becomes significant for distributed training. Multi-GPU and multi-node setups require high bandwidth. Cloud providers offer fast interconnect networks reducing communication overhead. Standard networking on distant regions introduces latency penalties.

Monitoring and operational overhead typically adds 5-10 percent to compute costs. CloudWatch, application logging, and infrastructure management consume resources. Efficient orchestration minimizes this overhead.

Failure recovery costs arise from interruptions. Spot instance failures require re-running interrupted work. Checkpoint frequency affects how much work is lost. Reliable checkpointing reduces recovery costs.

Cost Optimization Strategies

Spot instance utilization reduces costs 70-80 percent. Batch workloads running non-interactively benefit most. Checkpointing every 30 minutes enables recovering from interruptions. Spot instances cut a $95 (H100 PCIe) or $129 (H100 SXM) training cost to roughly $24-39, yielding enormous savings.

Reserved capacity commitments reduce costs 30-50 percent. One-year commitments to RunPod or Lambda provide volume discounts. Teams running continuous workloads benefit. Unused reservation capacity carries no refund, so commitment requires confidence.

Smaller GPU alternatives reduce training time costs. Using four A100s instead of one H100 might cost less total despite longer training duration. A100s cost $1.19-1.39 per hour depending on variant. Total costs depend on training duration sensitivity.

Batch size optimization reduces GPU hours required. Larger batches increase memory consumption but improve convergence. Finding the optimal batch size for target hardware reduces overall training time. Profiling reveals which batches are most efficient.

Model pruning and distillation reduce training requirements. Smaller models train faster on cheaper hardware. Knowledge distillation transfers large model knowledge to smaller variants. These techniques trade accuracy for speed and cost.

Mixed-precision training accelerates training. Using bfloat16 instead of float32 speeds operations 20-30 percent. Modern training frameworks support this automatically. Performance gains exceed accuracy losses for most models.

Regional Availability

US East 1 (N. Virginia) offers the most consistent H100 availability. All major providers maintain inventory in this region. Pricing is competitive as supply is more stable. Teams can reliably access H100s on demand.

US East 2 (Ohio) provides secondary availability. Most providers operate here but with less inventory than US East 1. Pricing is occasionally higher during peak demand. Teams should default to US East 1 when possible.

Availability varies by provider and time. RunPod and Lambda maintain consistent availability. AWS sometimes experiences temporary shortages during peak demand. Google Cloud availability fluctuates more significantly. Checking real-time availability before committing prevents disappointments.

Latency between US East 1 and distributed team members varies. Teams across the U.S. experience 10-50 millisecond latency from US East 1. International teams may experience higher latency. For non-interactive workloads, latency matters less.

Multi-region failure strategies distribute workloads. Critical systems use multiple regions for redundancy. Smaller workloads consolidate to cheapest region. Teams must balance cost against resilience.

FAQ

Q: Which H100 variant should I choose?

A: H100 SXM is faster due to better memory integration. H100 PCIe costs slightly less. Choose SXM if maximum throughput matters. Choose PCIe for budget-constrained projects. Performance differences are typically 5-15 percent depending on workload.

Q: How much can I save using spot instances?

A: Spot instances cost 25-30 percent of on-demand pricing. A $95 training session (H100 PCIe) costs roughly $24-29 on spot; a $129 session (H100 SXM) costs roughly $32-39. Interrupted training can lose progress, so checkpointing is essential. Batch workloads see the best cost savings.

Q: Should I buy a reserved instance for one-time training?

A: Reserved instances require one-year commitments. One-time projects prefer on-demand pricing. Paying upfront for unused capacity wastes money. Reserve capacity only for recurring workloads.

Q: What's the minimum training duration to justify H100 over A100?

A: H100's throughput advantage accelerates training 2-3x. For 48-hour training, H100 completes in 16-24 hours. Cost is roughly $137 (H100) versus $57-84 (A100). H100 justifies itself when time-to-market matters more than cost.

Q: Can I negotiate lower prices with providers?

A: Large teams can negotiate volume discounts. Long-term commitments yield better rates. Small teams find published pricing more favorable than negotiation. Spot instances often offer better savings than negotiation.

Understanding GPU pricing patterns helps select cost-effective infrastructure. Regional analysis identifies optimal deployment locations. Performance benchmarking guides hardware selection.

Review H100 specifications for hardware capabilities. Check RunPod GPU pricing for detailed rates. Study GPU pricing guide for comprehensive market analysis.

Sources