L40S on VERDA
Visit VERDAVERDA provides the NVIDIA L40S GPU, featuring 48GB GDDR6 VRAM on the Ada Lovelace architecture, optimized for enterprise data center workloads including visualization, high-performance computing, and AI inference/training. This offering stands out due to VERDA's green computing ethos: they capture waste heat from GPUs to supply district heating, slashing environmental impact for sustainable AI in Europe. Ideal for ML engineers and data scientists at eco-conscious organizations, it balances top-tier performance with reduced carbon emissions. Key value propositions include flexible per-hour billing for cost predictability, European data centers for low-latency regional access, and reliable infrastructure supporting demanding ML pipelines. While L40S excels in large model handling (e.g., up to 91 TFLOPS FP32), VERDA's sustainability focus appeals to teams tracking Scope 3 emissions, making it a noteworthy choice for green AI without performance compromises.
Why NVIDIA L40S on VERDA?
Opt for VERDA's NVIDIA L40S if sustainability drives your GPU decisions. VERDA repurposes data center waste heat for district heating, enabling carbon-neutral AI training in Europe—unique among providers. The L40S's 48GB VRAM and Ada Lovelace efficiency pair perfectly with this green infrastructure, supporting large-scale models like LLMs without excessive energy waste. Per-hour billing offers granular cost control for bursty ML workloads, outperforming commitment-based models. European locations minimize latency for continental users, and enterprise-tier reliability ensures uptime. This combo differentiates via eco-certifications and heat reuse, ideal for ESG-compliant teams seeking high VRAM without environmental trade-offs.
Live Pricing
Real-time NVIDIA L40S offers from VERDA
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
VERDA | 8×NVIDIA L40S 48GB VRAM | 48GB | 160 vCPU 480GB RAM | Helsinki | $0.91/GPU/hr $7.31/hr total (8×) | Sold Out | ||
VERDA | 8×NVIDIA L40S 48GB VRAM | 48GB | 160 vCPU 480GB RAM | Finland | $0.91/GPU/hr $7.31/hr total (8×) | Sold Out | ||
VERDA | 8×NVIDIA L40S 48GB VRAM | 48GB | 160 vCPU 480GB RAM | Finland | $0.91/GPU/hr $7.31/hr total (8×) | Sold Out | ||
VERDA | NVIDIA L40S 48GB VRAM | 48GB | 20 vCPU 60GB RAM | Finland | $0.91/GPU/hr | Sold Out | ||
VERDA | NVIDIA L40S 48GB VRAM | 48GB | 20 vCPU 60GB RAM | Finland | $0.91/GPU/hr | Sold Out |
Performance Notes
Expect NVIDIA L40S on VERDA to deliver near-native performance: 48GB VRAM handles large-batch training/inference on models up to 70B parameters; Ada Lovelace tensor cores provide ~1,500 TFLOPS sparse FP8 for efficient AI. Network bandwidth likely 100-400 Gbps Ethernet/InfiniBand (provider details sparse), supporting distributed training. Multi-GPU scaling via PCIe 4.0 probable, but NVLink or cluster interconnects unconfirmed—check docs for DGX-like setups. Storage options include fast NVMe SSDs for datasets. No public VERDA-specific benchmarks available, so performance mirrors standard L40S (e.g., MLPerf parity); real-world tests recommended. Honest limitations: interconnect and scaling details unknown publicly.
A provider focused on green computing using waste heat for district heating.
Best For
Unique Features
- Use of waste heat for district heating
- Green computing focus
VRAM
48GB
Architecture
Ada Lovelace
Tier
enterprise
Platform Features
Getting Started
Launching NVIDIA L40S on VERDA is user-friendly for ML pros. Sign up, configure via intuitive dashboard, and deploy in minutes. Pre-built images with CUDA, NVIDIA NGC containers, and Jupyter streamline workflows for AI training/inference.
Steps
- 1Sign up on VERDA website and complete identity verification (5-10 min).
- 2Access GPU catalog, select L40S instance type and European region.
- 3Configure vCPU/RAM, storage (NVMe SSD), and OS/image (Ubuntu/CUDA).
- 4Review per-hour pricing, then launch instance via one-click deploy.
- 5Connect via SSH/VNC, pull Docker images, and run your workload.
Pro Tips
- Leverage VERDA's sustainability dashboard to track and report waste heat reuse for ESG compliance.
- Use NVIDIA NGC catalogs for optimized L40S containers to accelerate setup and boost performance.
- Test multi-GPU scaling early; consult docs for interconnect support in training large models.
Frequently Asked Questions
What is VERDA's billing model for NVIDIA L40S?▾
VERDA bills per-hour for GPU instances including NVIDIA L40S. Hourly billing means you pay for full hours even if your job completes mid-hour. Plan your workloads accordingly to maximize cost efficiency.
Does VERDA offer spot instances for NVIDIA L40S?▾
No, VERDA 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 VERDA?▾
VERDA provides access to NVIDIA L40S instances via SSH, built-in Jupyter notebooks, programmatic API. 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. API access enables automation and integration with your existing ML pipelines and CI/CD workflows.
What compliance certifications does VERDA have for NVIDIA L40S workloads?▾
VERDA maintains GDPR, ISO 27001 certifications, making it suitable for regulated workloads. Contact VERDA directly for detailed compliance documentation and BAA agreements if needed.
Can I use NVIDIA L40S with Kubernetes on VERDA?▾
Yes, VERDA 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 VERDA best suited for?▾
The NVIDIA L40S on VERDA is well-suited for large-scale AI/ML training, LLM fine-tuning, batch inference at scale, and high-performance computing workloads. VERDA specifically excels at: Sustainable AI training in Europe. Consider your model size, training data volume, and latency requirements when evaluating this combination for your specific use case.
Does VERDA offer reserved instances for NVIDIA L40S?▾
Yes, VERDA 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 VERDA for current reserved pricing and commitment terms.
What unique features does VERDA offer for NVIDIA L40S?▾
VERDA differentiates itself with: Use of waste heat for district heating; Green computing focus. 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 VERDA?▾
To get started with NVIDIA L40S on VERDA, visit https://verda.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 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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