H100 PCIe on RunPod
Visit RunPodRunPod's NVIDIA H100 PCIe offering provides ML engineers and data scientists with accessible, high-performance access to the Hopper architecture's flagship GPU, featuring 80GB of HBM2e VRAM. This combination stands out for democratizing enterprise-grade AI workloads through RunPod's dual-tier model—Community Cloud for cost-sensitive experimentation and Secure Cloud for production needs. Key value propositions include per-second billing, spot instances for up to 70% savings, and FlashBoot technology enabling pod startups in under 90 seconds. Ideal for serverless inference on large language models, fine-tuning, and cost-effective prototyping, it lowers barriers to H100's capabilities like Transformer Engine for FP8 precision and 4x faster inference over A100. RunPod's infrastructure supports seamless scaling, making it a go-to for teams evaluating top-tier GPUs without long-term commitments. While community pods may share resources, secure options ensure dedicated performance, balancing cost and reliability for diverse AI pipelines.
Why NVIDIA H100 PCIe on RunPod?
Choose RunPod for NVIDIA H100 PCIe due to its alignment with the GPU's enterprise demands via specialized infrastructure. RunPod excels in cost-effective access with per-second billing and spot instances, reducing expenses for bursty ML workloads like LLM inference or training. FlashBoot minimizes downtime, complementing H100's high throughput. The dual-tier model offers flexibility: Community Cloud for rapid experimentation at low cost, Secure Cloud for isolated, production-ready environments. RunPod's optimized templates and Jupyter integration accelerate workflows, leveraging H100's 80GB VRAM for massive models without overprovisioning. Compared to hyperscalers, it provides faster onboarding and no egress fees, ideal for indie researchers and startups prioritizing agility over rigid contracts.
Live Pricing
Real-time NVIDIA H100 PCIe offers from RunPod
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 RunPod | NVIDIA H100 PCIe 80GB VRAM | 80GB | 16 vCPU 188GB RAM | 🌍global | $1.99/GPU/hr | Sold Out | ||
RunPod | NVIDIA H100 PCIe 80GB VRAM | 80GB | 16 vCPU 188GB RAM | 🌍global | $2.89/GPU/hr | Sold Out |
Performance Notes
On RunPod, expect near-native H100 PCIe performance with full Hopper features: up to 3,958 TFLOPS FP8 Tensor, 67 TFLOPS FP64, and Transformer Engine acceleration. PCIe 5.0 interface limits multi-GPU NVLink scaling to socketed configs (check pod specs for 2-8x options). Network bandwidth reaches 100Gbps Ethernet in secure pods; community varies. Storage includes high-IOPS NVMe SSDs (up to 8TB), suiting data-intensive tasks. Benchmarks show 1.5-2x inference speedups over A100 for LLMs like Llama 70B. Multi-GPU scaling is software-dependent via NCCL; actual throughput depends on workload. FlashBoot ensures consistent boot times, but spot interruptions possible—monitor via API for reliability.
A leader in democratized GPU space offering serverless inference and cost-effective experimentation.
Best For
Unique Features
- Dual-tier model (Community vs. Secure)
- FlashBoot technology
VRAM
80GB
Architecture
Hopper
Tier
enterprise
Platform Features
Getting Started
Getting started with RunPod's NVIDIA H100 PCIe is straightforward: sign up, fund your account, and deploy a pod via the intuitive dashboard. Supports templates for PyTorch, TensorFlow, or custom Docker images, with SSH, Jupyter, or TCP access for immediate workloads.
Steps
- 1Create a free RunPod account and verify email.
- 2Deposit funds via credit card or crypto for billing.
- 3Navigate to 'Pods', filter for H100 PCIe (80GB), select Community/Secure tier.
- 4Choose config (e.g., 1x GPU, storage), set spot/on-demand, and deploy.
- 5Connect via SSH/Jupyter link once FlashBoot completes (under 90s).
Pro Tips
- Opt for spot instances in Community Cloud to save 50-70% on experimentation, with auto-resume for interruptions.
- Use pre-built templates like RunPod's Stable Diffusion or Llama for instant H100-optimized setups.
- Monitor GPU utilization via dashboard; enable persistent storage for datasets to avoid re-uploads.
Frequently Asked Questions
What is RunPod's billing model for NVIDIA H100 PCIe?▾
RunPod bills per-second for GPU instances including NVIDIA H100 PCIe. 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 RunPod offer spot instances for NVIDIA H100 PCIe?▾
Yes, RunPod offers spot/preemptible instances for NVIDIA H100 PCIe, 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.
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