Finding the best l40s cloud provider requires balancing per-hour costs, memory capacity, and deployment infrastructure. The NVIDIA L40S GPU fills a critical position in cloud compute, offering 48GB of GDDR6 ECC memory on the Ada Lovelace architecture to bridge the gap between 24GB consumer cards and 80GB enterprise accelerators.
Key takeaways
- RunPod leads public L40S availability: RunPod Secure Cloud offers on-demand L40S instances starting at $0.99 per hour with per-minute billing and zero egress fees[source].
- 48GB VRAM sweet spot: The L40S delivers double the memory of an RTX 4090, enabling full 16-bit inference for 30B parameter models without model parallelism[source].
- Datacenter reliability without NVLink: Includes ECC memory and 362 FP16 TFLOPS, but lacks NVLink interconnects and Multi-Instance GPU (MIG) partitioning[source].
- Cost efficiency: At under $1.00 per hour, the L40S provides a lower hourly rate than H100 instances while hosting workloads that exceed 24GB VRAM limits[source].
NVIDIA L40S GPU specifications and hardware architecture
Understanding the hardware limits of the NVIDIA L40S helps determine whether it fits your AI workload before selecting a cloud host. Built on the 4nm Ada Lovelace architecture, the L40S is engineered primarily for AI inference, fine-tuning, 3D graphics rendering, and video processing[source].
| Specification | NVIDIA L40S Value |
|---|---|
| GPU Architecture | Ada Lovelace[source] |
| VRAM Capacity | 48GB GDDR6 with ECC[source] |
| Memory Bandwidth | 864 GB/s[source] |
| CUDA Cores | 18,176[source] |
| Tensor Cores | 568 (4th Generation)[source] |
| FP16 Tensor Performance | 362 TFLOPS (733 TFLOPS with Structural Sparsity)[source] |
| Thermal Design Power (TDP) | 350W[source] |
| Interconnect | PCIe Gen4 x16 (No NVLink)[source] |
| Video Encoders/Decoders | 3x NVENC, 3x NVDEC (includes AV1)[source] |
The 48GB VRAM buffer features Error-Correcting Code (ECC) functionality, preventing silent data corruption during long-running batch jobs or model serving[source]. However, unlike the flagship H100, the L40S relies solely on PCIe Gen4 system bandwidth. It does not support physical NVLink bridges, meaning multi-GPU training scales over system memory buses rather than high-speed direct GPU-to-GPU interconnects[source].
Evaluating the best l40s cloud providers
Publicly verifiable pricing for L40S cloud instances remains concentrated among specialized GPU providers. While large enterprise hosts offer custom quotes, on-demand self-service access is transparently priced on specialized platforms.
RunPod Secure Cloud
RunPod offers on-demand access to NVIDIA L40S GPUs within its Secure Cloud tier[source]. Instances are deployed as Docker containers or full virtual machines, featuring pre-configured PyTorch and CUDA environments[source].
- On-demand pricing: $0.99 per hour[source]
- Spot pricing: $0.79 per hour[source]
- System configuration: 16 vCPUs and 94GB system RAM per GPU[source]
- Billing model: Per-minute metering without long-term commitments[source]
- Network egress fees: $0.00 (unlimited standard bandwidth included)[source]
RunPod provides an ideal deployment path for teams needing fast spin-up times. For a detailed breakdown of RunPod infrastructure, see our comprehensive RunPod review.
Other providers such as CoreWeave list the L40S in their product catalogs, but require sales contracts or custom enterprise quotes rather than publishing flat hourly rates. Consequently, RunPod remains the leading transparent option for developers evaluating instant access[source].
Comparing the L40S to alternative cloud GPUs
To select the best l40s cloud instance, compare its technical capabilities against surrounding GPU classes in our GPU lookup tool.
L40S vs NVIDIA RTX 4090
The RTX 4090 is a popular choice for budget compute, but its 24GB non-ECC VRAM creates strict boundaries. The L40S doubles available memory to 48GB with ECC protection[source]. For developers running 30B parameter LLMs or large Stable Diffusion pipelines, the L40S eliminates out-of-memory errors without requiring aggressive model quantization. Read our guide on the best RTX 4090 cloud providers to evaluate smaller workloads.
L40S vs NVIDIA H100
The NVIDIA H100 features 80GB SXM5 memory and high-speed NVLink interconnects designed for large-scale cluster training[source]. However, enterprise H100 SXM instances on RunPod cost $2.69 per hour[source]. The L40S costs $0.99 per hour, providing a significantly lower entry cost for single-node inference workloads that do not require NVLink or 80GB VRAM buffers[source]. Review our overview of the best GPU cloud for inference to compare throughput metrics across enterprise cards.
Workload suitability and practical recommendations
The NVIDIA L40S excels in scenarios where consumer cards lack memory and enterprise flagship GPUs exceed budget requirements.
Large Language Model (LLM) inference
Running a 30B parameter LLM in 16-bit floating-point precision requires approximately 60GB of memory across multi-GPU setups, or fits comfortably on a single L40S when quantized to 8-bit or 4-bit precision[source]. The 864 GB/s memory bandwidth ensures rapid token generation for concurrent user requests[source].
Parameter-Efficient Fine-Tuning (PEFT)
For techniques such as LoRA and QLoRA, fine-tuning 70B models across a pair of L40S GPUs (96GB aggregate VRAM) provides a cost-effective alternative to renting multi-H100 nodes[source]. Learn more about training infrastructure in our guide to the best GPU cloud for ML training.
Video processing and 3D rendering
Equipped with three hardware encoders and three decoders supporting AV1, the L40S accelerates massive video transcoding and AI image generation workflows faster than standard datacenter cards lacking dedicated media engines[source].
Cost analysis for L40S GPU rentals
Renting an L40S on an on-demand basis at $0.99 per hour totals approximately $23.76 for a continuous 24-hour run[source]. Using spot instances at $0.79 per hour reduces daily compute costs to $18.96 for fault-tolerant batch processing[source].
Because cloud billing is metered by the minute, developers can execute temporary evaluation jobs lasting two hours for under $2.00[source]. This flexibility makes the L40S an economical platform for testing model deployments before committing to fixed monthly server reservations.
Who should choose the L40S GPU
The NVIDIA L40S is the right choice if:
- Your model weights and activation buffers exceed the 24GB limit of consumer cards[source].
- You require ECC memory to ensure numerical stability during multi-day jobs[source].
- You want on-demand cloud deployment at under $1.00 per hour without long-term contracts[source].
You should skip the L40S and select an H100 or A100 if:
- Your training workload relies on high-speed multi-GPU communication across NVLink bridges[source].
- You require hardware-level partitioning via Multi-Instance GPU (MIG) technology[source].
Research methodology and sources
GPU Picks relies strictly on published manufacturer specifications and verified cloud provider pricing. We do not perform hands-on benchmark measurements or report unverified performance metrics. Learn more about our evidence standards on our methodology page.
What is the hourly rental cost of an NVIDIA L40S GPU?
Does the NVIDIA L40S support NVLink?
No, the L40S uses a standard PCIe Gen4 x16 interface and does not support physical NVLink interconnects[source]. Multi-GPU scaling occurs across system PCIe buses.
How much VRAM does the L40S GPU have?
The L40S features 48GB of GDDR6 memory with Error-Correcting Code (ECC) support and a memory bandwidth of 864 GB/s[source].
Can the L40S fine-tune 70B parameter LLMs?
Yes, using multi-GPU nodes (such as 2x or 4x L40S configurations) or memory-efficient fine-tuning techniques like QLoRA, the 48GB VRAM buffer per card comfortably accommodates large model weights and gradients[source].
What is the TDP of the NVIDIA L40S?
The NVIDIA L40S has a Thermal Design Power (TDP) of 350 watts, utilizing a dual-slot passive cooling design suited for datacenter servers[source].