H200 SXM on Ori
Visit OriOri's NVIDIA H200 SXM offering combines a cutting-edge 141GB HBM3e VRAM GPU based on the Hopper architecture with Ori's specialized cloud-to-edge orchestration platform. This enterprise-tier GPU excels in AI training, inference, HPC, and data analytics, delivering up to 4.8 TB/s memory bandwidth and superior performance for large language models (LLMs) and multimodal workloads compared to the H100. Ori's focus on multi-cloud and edge AI makes this noteworthy for ML engineers and data scientists requiring seamless deployment across distributed environments, from data centers to edge devices. Key value propositions include per-second billing for cost-effective, flexible usage; unified orchestration avoiding silos; and massive VRAM enabling in-context learning on models exceeding 100B parameters without sharding. Ideal for teams prioritizing scalability, low-latency edge inference, and hybrid cloud strategies without vendor lock-in.
Why NVIDIA H200 SXM on Ori?
Choose Ori for NVIDIA H200 SXM if your workloads span multi-cloud and edge environments, leveraging Ori's cloud-to-edge platform for effortless orchestration. This combo shines for distributed AI pipelines, allowing H200's 141GB VRAM and Hopper tensor cores to power massive models deployable near data sources for reduced latency. Per-second billing optimizes costs for intermittent or experimental runs, complementing the GPU's high efficiency in FP8/FP16 tasks. Ori's architecture mitigates multi-cloud complexity, enabling NVLink-like scaling across providers, while supporting custom edge integrations—unique advantages over siloed GPU clouds.
Live Pricing
Real-time NVIDIA H200 SXM offers from Ori
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Ori | 2×NVIDIA H200 SXM 141GB VRAM | 141GB | 48 vCPU 480GB RAM 6000GB Storage | 🌍global | $3.50/GPU/hr $7.00/hr total (2×) | Sold Out | ||
Ori | 8×NVIDIA H200 SXM 141GB VRAM | 141GB | 192 vCPU 1920GB RAM 24000GB Storage | London | $3.50/GPU/hr $28.00/hr total (8×) | Sold Out | ||
Ori | NVIDIA H200 SXM 141GB VRAM | 141GB | 24 vCPU 240GB RAM 3000GB Storage | London | $3.50/GPU/hr | Sold Out | ||
Ori | 4×NVIDIA H200 SXM 141GB VRAM | 141GB | 96 vCPU 960GB RAM 12000GB Storage | London | $3.50/GPU/hr $14.00/hr total (4×) | Sold Out | ||
Ori | 2×NVIDIA H200 SXM 141GB VRAM | 141GB | 48 vCPU 480GB RAM 6000GB Storage | London | $3.50/GPU/hr $7.00/hr total (2×) | Sold Out |
Performance Notes
On Ori, expect NVIDIA H200 SXM to deliver flagship Hopper performance: ~4x faster LLM inference than H100 due to doubled HBM3e memory (141GB at 4.8 TB/s bandwidth), with peaks at 395 TFLOPS FP8 and 197 TFLOPS FP16. Network bandwidth and interconnects (e.g., InfiniBand/RoCE) are provider-specific and not publicly detailed—assume data center-grade for ML scaling, but verify via benchmarks. Multi-GPU scaling leverages Ori's orchestration for clusters; storage options likely include high-IOPS NVMe. Edge deployments may trade raw throughput for proximity. Real-world perf depends on config; Ori lacks extensive public benchmarks, so pilot testing recommended.
A provider focused on edge-to-cloud orchestration for multi-cloud and edge AI.
Best For
Unique Features
- Cloud-to-Edge platform architecture
VRAM
141GB
Architecture
Hopper
Tier
enterprise
Platform Features
Getting Started
Launching NVIDIA H200 SXM on Ori is streamlined through their intuitive dashboard, supporting quick provisioning across cloud or edge targets. New users benefit from per-second billing and orchestration tools, enabling rapid iteration on high-VRAM AI workloads without deep infra expertise.
Steps
- 1Sign up for an Ori account and complete identity verification via their portal.
- 2Navigate to the GPU marketplace, select NVIDIA H200 SXM, and choose instance size.
- 3Configure resources (vCPU/RAM, storage, network) and deployment target (cloud/edge cluster).
- 4Review per-second pricing, add orchestration policies if needed, and launch the instance.
- 5Access via SSH, Jupyter, or Ori's API; install CUDA 12.x and ML frameworks like PyTorch.
Pro Tips
- Use Ori's auto-scaling orchestration for dynamic multi-GPU clusters, optimizing H200 VRAM utilization during peak training loads.
- Colocate edge instances near data sources to minimize latency for real-time inference on large models.
- Monitor costs closely with per-second billing; script shutdowns for idle instances to maximize savings.
Frequently Asked Questions
What is Ori's billing model for NVIDIA H200 SXM?▾
Ori bills per-second for GPU instances including NVIDIA H200 SXM. 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 Ori offer spot instances for NVIDIA H200 SXM?▾
No, Ori does not currently offer spot instances for NVIDIA H200 SXM. 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.
Does Ori offer reserved instances for NVIDIA H200 SXM?▾
Yes, Ori offers reserved instance pricing for NVIDIA H200 SXM, 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 Ori for current reserved pricing and commitment terms.
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