L40S on RunPod
Visit RunPodRunPod's NVIDIA L40S offering combines a leading democratized GPU provider with a high-performance enterprise data center GPU, delivering 48GB GDDR6 VRAM on the Ada Lovelace architecture. Ideal for ML engineers tackling demanding AI inference, fine-tuning large models, visualization, and compute workloads, this setup excels in serverless inference and cost-effective experimentation. RunPod's dual-tier model—Community Cloud for affordable shared access and Secure Cloud for production-grade isolation—paired with FlashBoot technology for sub-100-second pod spin-up times, minimizes latency and overhead. Per-second billing and spot instances enable precise cost control, often 50-70% cheaper than on-demand alternatives. The L40S provides robust performance with up to 91 TFLOPS FP32, 182 TFLOPS FP16, and advanced features like DLSS and RTX for real-time rendering. This combination empowers rapid prototyping, scalable inference endpoints, and efficient resource utilization without long-term commitments, making it a go-to for data scientists evaluating flexible GPU options.
Why NVIDIA L40S on RunPod?
Choose RunPod for NVIDIA L40S due to its alignment with the GPU's strengths in AI, compute, and visualization. RunPod's serverless inference endpoints leverage the L40S's 48GB VRAM for handling massive models like Llama 70B or Stable Diffusion XL without swapping. FlashBoot ensures near-instant pod availability, complementing the L40S's high throughput for low-latency workloads. Per-second billing and spot instances (up to 80% discounts) maximize cost-efficiency for bursty experimentation, while dual-tier options provide flexibility—from Community Cloud's low-cost sharing to Secure Cloud's VPC isolation. RunPod's optimized templates with CUDA 12.x, Docker support, and auto-scaling enhance the L40S's Ada Lovelace capabilities, offering superior accessibility over traditional hyperscalers.
Live Pricing
Real-time NVIDIA L40S offers from RunPod
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 RunPod | NVIDIA L40S 48GB VRAM | 48GB | 16 vCPU 94GB RAM | 🌍global | $0.79/GPU/hr | Available | ||
RunPod | NVIDIA L40S 48GB VRAM | 48GB | 16 vCPU 94GB RAM | 🌍global | $1.09/GPU/hr | Available |
Performance Notes
On RunPod, the NVIDIA L40S delivers strong performance for AI workloads, with 48GB VRAM supporting models up to 40B+ parameters in FP16. Expect 90+ TFLOPS FP32 and 700+ TFLOPS sparse Tensor Core performance, ideal for inference and training. Network bandwidth reaches 100Gbps+ in Secure Cloud pods, enabling efficient multi-GPU scaling via NVLink or InfiniBand in multi-GPU configs. Storage includes fast NVMe SSDs (up to 4TB), reducing data loading times. FlashBoot achieves <90s startup, minimizing cold starts. Benchmarks show competitive throughput vs. A100/H100 for inference, though exact figures vary by workload and config. Multi-GPU scaling is solid but provider-dependent; test for your use case as interconnect details aren't fully public.
A leader in democratized GPU space offering serverless inference and cost-effective experimentation.
Best For
Unique Features
- Dual-tier model (Community vs. Secure)
- FlashBoot technology
VRAM
48GB
Architecture
Ada Lovelace
Tier
enterprise
Platform Features
Getting Started
Getting started with NVIDIA L40S on RunPod is straightforward, leveraging an intuitive dashboard for instant pod deployment. New users can launch serverless endpoints or on-demand pods in minutes, with pre-configured ML templates accelerating workflows from experimentation to production inference.
Steps
- 1Sign up for a RunPod account and add payment method for per-second billing.
- 2Navigate to 'Pods' or 'Serverless', select NVIDIA L40S GPU and desired config (e.g., 48GB VRAM).
- 3Choose Community or Secure Cloud, select a template like PyTorch or Jupyter.
- 4Configure storage/volume, set spot/on-demand pricing, and deploy with FlashBoot.
- 5Connect via SSH/Jupyter/HTTP and start your workload.
Pro Tips
- Opt for spot instances in Community Cloud to slash costs by 50-80% for non-critical experiments.
- Use FlashBoot and serverless endpoints for sub-minute inference scaling without managing infrastructure.
- Pre-load datasets to persistent volumes to avoid repeated transfers and optimize startup times.
Frequently Asked Questions
What is RunPod's billing model for NVIDIA L40S?▾
RunPod 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 RunPod offer spot instances for NVIDIA L40S?▾
Yes, RunPod 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.
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