H100 PCIe on Paperspace
Visit PaperspacePaperspace offers the NVIDIA H100 PCIe GPU, equipped with 80GB HBM3 VRAM and Hopper architecture, optimized for AI training, inference, data analytics, and HPC workloads. This enterprise-tier GPU delivers up to 3.35 petaFLOPS FP8 and 67 teraFLOPS FP16 tensor performance, enabling efficient handling of large-scale models like LLMs. What makes this noteworthy is Paperspace's Gradient MLOps platform, which integrates notebooks, experiments, deployments, and monitoring into a unified workflow, ideal for individual developers, educators, and small teams. Per-second billing ensures cost flexibility for bursty or exploratory workloads, avoiding overprovisioning. Key value propositions include instant provisioning, pre-configured ML environments (PyTorch, TensorFlow), collaborative sharing, and seamless scaling from prototyping to production—empowering users to access flagship GPU power without infrastructure management overhead.
Why NVIDIA H100 PCIe on Paperspace?
Paperspace paired with NVIDIA H100 PCIe excels for developers needing end-to-end ML simplicity. Gradient's platform uniquely streamlines notebook-to-deployment pipelines, leveraging the H100's 80GB VRAM for massive datasets and models without workflow friction. Per-second billing suits variable research loads, reducing costs versus hourly commitments. Provider strengths like user-friendly UI, education-focused pricing tiers, and quick setup complement the GPU's Hopper capabilities for FP8/Transformer Engine acceleration. Unlike enterprise clouds, Paperspace prioritizes accessibility for individuals, offering pre-built templates and integrations that minimize ramp-up time, making high-end AI compute practical for solo practitioners or classrooms.
Live Pricing
Real-time NVIDIA H100 PCIe offers from Paperspace
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
![]() Paperspace | NVIDIA H100 PCIe 80GB VRAM | 80GB | 20 vCPU 256GB RAM 50GB Storage | Canada | $5.95/GPU/hr | Sold Out | ||
![]() Paperspace | 8×NVIDIA H100 PCIe 80GB VRAM | 80GB | 160 vCPU 2048GB RAM 50GB Storage | Canada | $5.95/GPU/hr $47.60/hr total (8×) | Sold Out | ||
![]() Paperspace | NVIDIA H100 PCIe 80GB VRAM | 80GB | 20 vCPU 256GB RAM 50GB Storage | Amsterdam | $5.95/GPU/hr | Sold Out | ||
![]() Paperspace | 8×NVIDIA H100 PCIe 80GB VRAM | 80GB | 160 vCPU 2048GB RAM 50GB Storage | Amsterdam | $5.95/GPU/hr $47.60/hr total (8×) | Sold Out | ||
![]() Paperspace | 8×NVIDIA H100 PCIe 80GB VRAM | 80GB | 160 vCPU 2048GB RAM 50GB Storage | New York | $5.95/GPU/hr $47.60/hr total (8×) | Available |





Performance Notes
On Paperspace, the H100 PCIe provides top-tier Hopper performance: 4,000+ TOPS AI inference, 2x faster than A100 for LLMs. Expect strong single-GPU results for 70B+ parameter models via 80GB VRAM. Network bandwidth (likely 100-400Gb/s Ethernet/InfiniBand) and NVMe storage support efficient data loading, but exact specs per instance are undocumented—contact support for details. Multi-GPU scaling available in larger configs, though PCIe limits NVLink bandwidth vs. SXM variants. Benchmarks show excellent CUDA 12+ compatibility; real-world throughput depends on optimization. No public Paperspace-specific H100 metrics yet, so test for your workload.
A provider offering the Gradient MLOps platform for simplifying notebook-to-deployment workflows.
Best For
Unique Features
- Gradient platform for ML workflows
VRAM
80GB
Architecture
Hopper
Tier
enterprise
Platform Features
Getting Started
Launch NVIDIA H100 PCIe on Paperspace via the intuitive Gradient console. Free signup leads to instant access; select H100 configs with 80GB VRAM for AI tasks. Use Jupyter notebooks or SSH, with per-second billing starting immediately upon creation.
Steps
- 1Sign up at paperspace.com/gradient and add billing details.
- 2Go to Notebooks or Machines; filter for H100 PCIe (80GB).
- 3Select runtime (e.g., PyTorch 2.1), storage (100GB+ NVMe), and launch.
- 4Access via browser Jupyter/SSH; install packages with pip/conda.
- 5Run workloads; pause instance to stop billing.
Pro Tips
- Leverage Gradient Experiments for hyperparameter tracking and reproducibility across H100 sessions.
- Attach persistent Volumes for datasets to avoid re-uploads and speed up iterations.
- Use Docker templates for custom envs; monitor GPU util with nvidia-smi for optimization.
Frequently Asked Questions
What is Paperspace's billing model for NVIDIA H100 PCIe?▾
Paperspace 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 Paperspace offer spot instances for NVIDIA H100 PCIe?▾
No, Paperspace does not currently offer spot instances for NVIDIA H100 PCIe. All instances are billed at on-demand rates. However, they do offer reserved instances for committed usage, which can provide significant discounts for long-term workloads.
How can I access NVIDIA H100 PCIe instances on Paperspace?▾
Paperspace provides access to NVIDIA H100 PCIe instances via SSH, built-in Jupyter notebooks, web-based terminal, Docker containers. The built-in Jupyter notebook support makes it easy to start experimenting immediately without additional setup. SSH access gives you full control over the instance for custom configurations and production deployments.
What compliance certifications does Paperspace have for NVIDIA H100 PCIe workloads?▾
Paperspace maintains SOC 2, GDPR certifications, making it suitable for regulated workloads. SOC 2 certification demonstrates strong security controls for handling sensitive data. Contact Paperspace directly for detailed compliance documentation and BAA agreements if needed.
Can I use NVIDIA H100 PCIe with Kubernetes on Paperspace?▾
Paperspace does not prominently advertise native Kubernetes support. You may need to manage your own Kubernetes cluster or use alternative orchestration methods. However, they do support Docker containers, which can be a stepping stone to container orchestration.
What are the specifications of the NVIDIA H100 PCIe?▾
The NVIDIA H100 PCIe features 80GB of high-bandwidth memory, built on NVIDIA's Hopper architecture. As an enterprise-tier GPU, it's designed for large-scale AI training, inference at scale, and demanding HPC workloads. The substantial VRAM capacity supports large language models, complex neural networks, and multi-model deployments.
What workloads is NVIDIA H100 PCIe on Paperspace best suited for?▾
The NVIDIA H100 PCIe on Paperspace is well-suited for large-scale AI/ML training, LLM fine-tuning, batch inference at scale, and high-performance computing workloads. Paperspace specifically excels at: Individual developers and education. Consider your model size, training data volume, and latency requirements when evaluating this combination for your specific use case.
Does Paperspace offer reserved instances for NVIDIA H100 PCIe?▾
Yes, Paperspace offers reserved instance pricing for NVIDIA H100 PCIe, 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 Paperspace for current reserved pricing and commitment terms.
What unique features does Paperspace offer for NVIDIA H100 PCIe?▾
Paperspace differentiates itself with: Gradient platform for ML workflows. These features may provide advantages depending on your specific workflow requirements and technical needs. Evaluate how these capabilities align with your ML infrastructure goals when making your decision.
How do I get started with NVIDIA H100 PCIe on Paperspace?▾
To get started with NVIDIA H100 PCIe on Paperspace, visit https://www.paperspace.com?utm_source=gpuperhour&utm_medium=referral to create an account. Most providers offer a straightforward signup process, and some provide initial credits for new users. Once registered, you can typically launch a NVIDIA H100 PCIe instance within minutes through their dashboard or API. We recommend starting with a small experiment to familiarize yourself with the platform before scaling up to larger workloads.
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