H100 NVL on RunPod
Visit RunPodRunPod's NVIDIA H100 NVL offering delivers enterprise-grade performance with 94GB HBM3 VRAM on the Hopper architecture, optimized for demanding AI, large language model inference, and HPC workloads. As a leader in democratized GPU access, RunPod combines this powerhouse GPU with its dual-tier model—Community Cloud for affordable experimentation and Secure Cloud for production security—alongside FlashBoot technology for sub-90-second instance launches. This makes it noteworthy for ML engineers and data scientists seeking cost-effective scaling without infrastructure overhead. Key value propositions include per-second billing, spot instances for up to 50% savings, NVLink-enabled multi-GPU interconnects for efficient training, and seamless support for frameworks like PyTorch and TensorFlow. Target users benefit from high memory bandwidth (up to 3.35 TB/s), enabling deployment of massive models like GPT-4 scale or Llama 70B+ without sharding compromises, all while maintaining flexibility for serverless inference and rapid prototyping.
Why NVIDIA H100 NVL on RunPod?
RunPod pairs exceptionally well with the H100 NVL due to its focus on serverless inference and cost-effective experimentation, amplifying the GPU's 94GB VRAM and Hopper FP8/INT8 tensor core advantages for large-model workloads. FlashBoot technology minimizes deployment latency, ideal for iterative ML development, while per-second billing and spot instances optimize costs for bursty training or inference—often 30-50% cheaper than on-demand competitors. The dual-tier model offers Community Cloud for quick tests and Secure Cloud for compliant production, complementing NVLink for multi-GPU scaling. RunPod's 1,000+ optimized templates accelerate setup, reducing time-to-value for PyTorch, Hugging Face, or vLLM users compared to more rigid providers.
Live Pricing
Real-time NVIDIA H100 NVL offers from RunPod
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 RunPod | NVIDIA H100 NVL 94GB VRAM | 94GB | 16 vCPU 94GB RAM | 🌍global | $2.59/GPU/hr | Sold Out | ||
RunPod | NVIDIA H100 NVL 94GB VRAM | 94GB | 16 vCPU 94GB RAM | 🌍global | $3.19/GPU/hr | Sold Out |
Performance Notes
RunPod's H100 NVL delivers Hopper architecture peaks: ~4 PFLOPS FP8, 3.35 TB/s HBM3 bandwidth, excelling in single-GPU inference for 70B+ models and fine-tuning. Multi-GPU NVLink (up to 900 GB/s bidirectional) supports efficient scaling in 4-8 GPU pods; Secure Cloud offers 400 Gbps InfiniBand for clusters. Storage includes NVMe SSDs (up to 100 TB) with 10-20 GB/s throughput. FlashBoot ensures near-instant warm starts. Benchmarks show competitive TFLOPS vs. on-prem, but multi-node all-reduce performance varies by workload—user reports confirm strong vLLM inference. Unknowns: exact pod interconnect latency without custom testing; recommend benchmarking for distributed training.
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
94GB
Architecture
Hopper
Tier
enterprise
Platform Features
Getting Started
Launching NVIDIA H100 NVL on RunPod is user-friendly via the web dashboard. New users can deploy pods in minutes using pre-configured templates, with options for Community or Secure Cloud, spot/on-demand pricing, and instant access via Jupyter or SSH—perfect for quick ML experimentation.
Steps
- 1Sign up for a RunPod account and add funds using credit card, PayPal, or crypto.
- 2Go to 'Pods' dashboard, filter for 'H100 NVL' GPU, and select Community or Secure Cloud.
- 3Choose spot or on-demand pricing, configure disk size (e.g., 100GB NVMe), and select a template like RunPod PyTorch.
- 4Click 'Deploy' to launch instantly with FlashBoot, then connect via TCP proxy, HTTP, or JupyterLab.
- 5Install dependencies and run workloads; scale by deploying multi-GPU configurations as needed.
Pro Tips
- Opt for spot instances in Community Cloud for 30-50% cost savings on non-urgent inference or prototyping workloads.
- Use official RunPod templates (e.g., vLLM or Stable Diffusion) to skip Docker setup and start training immediately.
- Enable auto-suspend after idle time to leverage per-second billing and avoid unnecessary charges during experiments.
Frequently Asked Questions
What is RunPod's billing model for NVIDIA H100 NVL?▾
RunPod bills per-second for GPU instances including NVIDIA H100 NVL. 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 NVL?▾
Yes, RunPod offers spot/preemptible instances for NVIDIA H100 NVL, 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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