L40S on Ori
Visit OriOri's NVIDIA L40S offering combines a high-performance enterprise GPU with a specialized edge-to-cloud orchestration platform, ideal for ML engineers tackling distributed AI workloads across multi-cloud and edge environments. The L40S, built on NVIDIA's Ada Lovelace architecture, delivers 48GB GDDR6 VRAM, exceptional FP8/FP16 tensor performance (up to 1,821 TFLOPS FP8), and robust support for visualization, compute, and generative AI tasks. Ori's Cloud-to-Edge architecture enables seamless deployment from central clouds to distributed edge nodes, optimizing latency-sensitive inference and training pipelines. This is noteworthy for teams requiring flexible orchestration without vendor lock-in, per-second billing for cost efficiency, and integration with Kubernetes for scalable AI ops. Target audience includes data scientists and DevOps engineers building real-time AI applications like autonomous systems or edge analytics, where the L40S's balanced compute-visualization profile shines alongside Ori's hybrid infrastructure strengths.
Why NVIDIA L40S on Ori?
Choose Ori for NVIDIA L40S if your workflows demand edge-to-cloud continuity, as Ori's platform excels in multi-cloud orchestration, allowing L40S instances to span data centers and edge devices without reconfiguration. The GPU's 48GB VRAM and Ada Lovelace efficiency pair perfectly with Ori's low-latency networking for distributed training/inference. Per-second billing minimizes costs for bursty workloads, unlike hourly models. Unique advantages include native Kubernetes support for multi-GPU scaling and edge deployment tools, complementing L40S's enterprise features like NVLink and multi-instance GPU. Ideal for avoiding silos in hybrid AI setups.
Live Pricing
Real-time NVIDIA L40S offers from Ori
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
![]() Ori | 8×NVIDIA L40S 48GB VRAM | 48GB | 128 vCPU 1920GB RAM 3400GB Storage | 🌍global | $1.55/GPU/hr $12.40/hr total (8×) | Sold Out | ||
![]() Ori | NVIDIA L40S 48GB VRAM | 48GB | 16 vCPU 240GB RAM 1600GB Storage | 🌍global | $1.55/GPU/hr | Sold Out | ||
![]() Ori | 4×NVIDIA L40S 48GB VRAM | 48GB | 64 vCPU 960GB RAM 2600GB Storage | 🌍global | $1.55/GPU/hr $6.20/hr total (4×) | Sold Out | ||
![]() Ori | 8×NVIDIA L40S 48GB VRAM | 48GB | 128 vCPU 1920GB RAM 3400GB Storage | 🌍global | $1.55/GPU/hr $12.40/hr total (8×) | Sold Out | ||
![]() Ori | NVIDIA L40S 48GB VRAM | 48GB | 15 vCPU 90GB RAM 400GB Storage | 🌍global | $1.55/GPU/hr | Sold Out |





Performance Notes
On Ori, expect L40S to deliver near-native Ada Lovelace performance: ~91 TFLOPS FP32, 1,821 TFLOPS FP8 for AI training/inference, with 48GB VRAM suiting large models like Llama 70B. Network bandwidth likely 100-400 Gbps (provider specifics unconfirmed), supporting efficient multi-GPU via NVLink/SLI. Storage options include high-IOPS NVMe, but edge deployments may vary. Multi-GPU scaling is feasible via Kubernetes, though real-world benchmarks are limited—assume 80-95% efficiency based on similar providers. Edge latency optimizations enhance inference; test for your workload as orchestration overhead is minimal but unquantified.
A provider focused on edge-to-cloud orchestration for multi-cloud and edge AI.
Best For
Unique Features
- Cloud-to-Edge platform architecture
VRAM
48GB
Architecture
Ada Lovelace
Tier
enterprise
Platform Features
Getting Started
Getting started with NVIDIA L40S on Ori is straightforward via their web console or CLI, leveraging the Cloud-to-Edge platform for quick instance spins. Sign up, configure GPU-accelerated nodes, and deploy AI workloads with per-second billing kicking in immediately. Focus on selecting edge/cloud regions for optimal latency.
Steps
- 1Create an Ori account and verify via email or SSO.
- 2Navigate to the console, select 'Launch Instance' and choose NVIDIA L40S (48GB) configuration.
- 3Pick region (cloud or edge), instance size, storage, and networking options.
- 4Deploy with pre-built ML images (e.g., NVIDIA NGC containers) or custom Docker/K8s.
- 5Access via SSH/Jupyter and monitor via Ori dashboard.
Pro Tips
- Use Ori's orchestration tools to auto-scale L40S clusters across edge-cloud for cost-optimized training.
- Leverage per-second billing by scripting short-lived inference jobs; integrate with Kubernetes for multi-GPU.
- Optimize for edge with L40S's visualization cores—test NVLink for 2+ GPU setups early.
Frequently Asked Questions
What is Ori's billing model for NVIDIA L40S?▾
Ori bills per-second for GPU instances including NVIDIA L40S. 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 L40S?▾
No, Ori does not currently offer spot instances for NVIDIA L40S. 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 L40S instances on Ori?▾
Ori provides access to NVIDIA L40S instances via SSH, built-in Jupyter notebooks, web-based terminal. 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 Ori have for NVIDIA L40S workloads?▾
Ori maintains SOC 2, GDPR, ISO 27001 certifications, making it suitable for regulated workloads. SOC 2 certification demonstrates strong security controls for handling sensitive data. Contact Ori directly for detailed compliance documentation and BAA agreements if needed.
Can I use NVIDIA L40S with Kubernetes on Ori?▾
Yes, Ori supports Kubernetes for orchestrating NVIDIA L40S workloads. This enables you to deploy scalable ML pipelines, manage distributed training jobs across multiple GPUs, and integrate with MLOps tools like Kubeflow, Argo Workflows, and KServe. Kubernetes support is essential for teams building production-grade ML infrastructure.
What are the specifications of the NVIDIA L40S?▾
The NVIDIA L40S features 48GB of high-bandwidth memory, built on NVIDIA's Ada Lovelace 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 L40S on Ori best suited for?▾
The NVIDIA L40S on Ori is well-suited for large-scale AI/ML training, LLM fine-tuning, batch inference at scale, and high-performance computing workloads. Ori specifically excels at: Multi-cloud and edge AI orchestration. Consider your model size, training data volume, and latency requirements when evaluating this combination for your specific use case.
Does Ori offer reserved instances for NVIDIA L40S?▾
Yes, Ori offers reserved instance pricing for NVIDIA L40S, 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.
What unique features does Ori offer for NVIDIA L40S?▾
Ori differentiates itself with: Cloud-to-Edge platform architecture. 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 L40S on Ori?▾
To get started with NVIDIA L40S on Ori, visit https://ori.co?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 L40S 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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