L40S on Nebius
Visit NebiusNebius, an AI-centric infrastructure provider, delivers the NVIDIA L40S GPU with 48GB GDDR6 VRAM on its Ada Lovelace architecture, tailored for enterprise-grade visualization, compute, and AI workloads. This combination stands out for enterprises requiring EU/US data compliance alongside managed Kubernetes orchestration. As a publicly traded company with startup agility, Nebius emphasizes transparency and AI innovation, offering per-second billing and spot instances for cost optimization. The L40S excels in large language model inference, generative AI, and graphics-intensive tasks, delivering up to 1.4x better inference performance than predecessors via FP8 precision and Transformer Engine support. Key value propositions include seamless scaling in compliant environments, high VRAM for handling 70B+ parameter models, and integration with Nebius's AI-optimized clusters. Ideal for ML engineers at regulated firms seeking reliable, production-ready GPU compute without sovereignty risks, this offering balances raw power with operational simplicity and economic flexibility.
Why NVIDIA L40S on Nebius?
Choose Nebius for NVIDIA L40S when compliance and managed infrastructure are priorities. Nebius's EU/US data residency ensures regulatory adherence for enterprises in finance, healthcare, or government. Its managed Kubernetes simplifies multi-GPU deployments, complementing the L40S's enterprise-tier reliability for inference-heavy AI pipelines. Per-second billing and spot instances minimize costs for variable workloads, leveraging the GPU's 48GB VRAM for memory-intensive tasks like fine-tuning or rendering. Unique advantages include public company transparency for audit trails, AI-focused optimizations like pre-configured NGC containers, and high-availability clusters that amplify L40S scalability without custom DevOps overhead.
Live Pricing
Real-time NVIDIA L40S offers from Nebius
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
Nebius | NVIDIA L40S 48GB VRAM | 48GB | 8 vCPU 32GB RAM | 🌍Europe | $1.55/GPU/hr | |||
Nebius | NVIDIA L40S 48GB VRAM | 48GB | 16 vCPU 96GB RAM | 🌍Europe | $1.82/GPU/hr |
Performance Notes
On Nebius, the NVIDIA L40S delivers strong performance for AI inference and training, with 91 TFLOPS FP32, 182 TFLOPS FP16, and up to 733 TFLOPS INT8. Expect efficient multi-GPU scaling via NVLink or InfiniBand interconnects (up to 400Gbps in clusters), supporting distributed training for models up to 100B parameters. NVMe storage options provide low-latency I/O for datasets. Known strengths: excellent for vLLM or TensorRT-LLM inference. Limitations: specific Nebius benchmarks are sparse; real-world throughput depends on workload optimization. Single-node L40S suits prototyping, while clusters excel in production—test with spot instances for validation.
An AI-centric infrastructure company providing managed services for EU/US compliant workloads.
Best For
Unique Features
- Public company with transparency
- Startup-like focus on AI
VRAM
48GB
Architecture
Ada Lovelace
Tier
enterprise
Platform Features
Getting Started
Getting started with NVIDIA L40S on Nebius is straightforward via their console or Kubernetes API. Sign up for a compliant account, deploy managed GPU nodes, and access pre-built AI environments. Leverage per-second billing for quick experimentation.
Steps
- 1Create a Nebius account and enable billing for EU/US regions.
- 2Launch a Kubernetes cluster via console, selecting L40S GPU node pools.
- 3Configure node specs (e.g., 1-8 GPUs, NVMe storage) and deploy.
- 4Pull NGC containers (e.g., NVIDIA PyTorch) and SSH or kubectl into pods.
- 5Run workloads with nvidia-smi verification and scale as needed.
Pro Tips
- Use spot instances for non-critical training to cut costs by up to 70% while testing L40S inference throughput.
- Enable Nebius AI accelerators like autoscaling K8s for efficient multi-L40S training on large datasets.
- Optimize with FP8/Transformer Engine for 1.5x faster LLM inference on 48GB VRAM.
Frequently Asked Questions
What is Nebius's billing model for NVIDIA L40S?▾
Nebius 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 Nebius offer spot instances for NVIDIA L40S?▾
Yes, Nebius offers spot/preemptible instances for NVIDIA L40S, 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.
How can I access NVIDIA L40S instances on Nebius?▾
Nebius 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 Nebius have for NVIDIA L40S workloads?▾
Nebius maintains SOC 2, HIPAA, GDPR, ISO 27001 certifications, making it suitable for regulated workloads. HIPAA compliance is particularly important for healthcare and medical AI applications. SOC 2 certification demonstrates strong security controls for handling sensitive data. Contact Nebius directly for detailed compliance documentation and BAA agreements if needed.
Can I use NVIDIA L40S with Kubernetes on Nebius?▾
Yes, Nebius 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 Nebius best suited for?▾
The NVIDIA L40S on Nebius is well-suited for large-scale AI/ML training, LLM fine-tuning, batch inference at scale, and high-performance computing workloads. Nebius specifically excels at: Enterprises needing EU/US compliance and managed K8s. Consider your model size, training data volume, and latency requirements when evaluating this combination for your specific use case.
Does Nebius offer reserved instances for NVIDIA L40S?▾
Yes, Nebius 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 Nebius for current reserved pricing and commitment terms.
What unique features does Nebius offer for NVIDIA L40S?▾
Nebius differentiates itself with: Public company with transparency; Startup-like focus on AI. 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 Nebius?▾
To get started with NVIDIA L40S on Nebius, visit https://nebius.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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