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GPU → L40 vs L40S
NVIDIA L40 vs L40s and how to choose
Training an AI model using machine or deep learning requires processing massive amounts of data. Speed is critical and any performance degradation also impacts the effectiveness of the model training process.
But there are several options available for GPU chips, so which do you need? More GPU power is usually better, but you don’t want to spend more than you need to either.
Two of the most popular are NVIDIA’s L40 and L40S. They’re very similar, but there are some key differences. Let’s look at them both, and compare them side-by-side, so you can choose the best option for your needs.
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The NVIDIA L40
The NVIDIA L40 is a high-performance GPU built on the Ada Lovelace architecture, designed for AI, graphics rendering, and virtualization in data centers and enterprise environments.
The L40 features 48GB of GDDR6 memory, third-generation RT cores, and fourth-generation Tensor cores, which makes it ideal for AI inference, deep learning, and ray-traced graphics. It is also optimized for high-performance computing (HPC) and cloud-based workloads, because it offers improved power efficiency and scalability for demanding apps.
Targeted at AI developers, 3D designers, and cloud servers, the L40 is used in virtual workstations, CAD applications, and machine learning training.
It’s a successor to NVIDIA’s A40, with better performance for AI workloads and rendering.
Upgrade: NVIDIA L40S
The NVIDIA L40S is a high-performance data center GPU designed for AI, deep learning, graphics rendering, and high-performance computing (HPC).
It’s similar to the L40 in many ways: built on the Ada Lovelace architecture, 48GB of GDDR6 memory, third-generation RT cores, and Tensor cores (although enhanced over the L40 Tensor cores). Compared to the L40, however, the L40S provides greater compute power, making it an even better option for AI-heavy workloads, large-scale simulations, and accelerated cloud computing.
The L40S is usually used for generative AI, virtual desktop infrastructure (VDI), and real-time 3D rendering. It significantly outperforms the standard L40 in AI model training and data processing, making it a better choice for organizations that are pursuing cutting-edge AI acceleration. While both GPUs are optimized for professional workloads, the L40S is a more powerful and versatile option for intensive tasks.
L40 vs L40s: Side-by-side comparison
Here are the key differences between the NVIDIA L40 vs NVIDIA L40S:
- Performance: The L40S offers significantly higher compute power than the L40, making it better suited for AI training, inference, and high-performance computing (HPC) tasks.
- Graphics and rendering: Both GPUs feature third-generation RT cores for ray tracing, but the L40S is better optimized for real-time rendering and complex simulations.
- Target use cases: The L40 is well-suited for virtualization, CAD, and 3D content creation, whereas the L40S is optimized for AI-heavy workloads, deep learning, and cloud-based AI services.
- Compute workloads: The L40S handles more intensive compute workloads, making it a stronger option for scientific simulations, AI research, and enterprise-level AI deployments.
| Feature | NVIDIA L40 | NVIDIA L40S |
|---|---|---|
| Architecture | Ada Lovelace | Ada Lovelace |
| Memory | 48GB GDDR6 | 48GB GDDR6 |
| Ray tracing (RT Cores) | 3rd Gen RT Cores | 3rd Gen RT Cores |
| Tensor cores | 4th Gen Tensor Cores | Enhanced 4th Gen Tensor Cores |
| Compute performance | High | Higher (More AI/Compute Optimized) |
| AI and deep learning | Strong AI capabilities | Optimized for AI-heavy workloads (faster model training & inference) |
| Rendering performance | Great for CAD, 3D design, and VFX | Better for real-time ray tracing and complex simulations |
| Power efficiency | Lower power consumption | Higher power draw due to increased performance |
| Best for | Virtualization, 3D content creation, CAD, cloud graphics | AI research, deep learning, generative AI, high-performance computing (HPC) |
| Use cases | Workstations, cloud-based applications, rendering | AI-driven applications, enterprise AI, large-scale simulations |
L40 vs L40S: Which do you need?
Deciding between an NVIDIA L40 server and an NVIDIA L40S server depends on your workload requirements, budget, and use case priorities. Here’s a structured way to determine which one is best for you:
1. Identify your primary workload
If your workload is graphics-heavy (3D rendering, CAD, VFX, virtualization, etc.), the L40 is likely the better fit.
But if your workload is AI-intensive (machine learning, deep learning, AI model training, HPC, etc.), you should go with the L40S.
2. Consider performance vs cost
The L40 comes in at a lower price point and provides strong performance for general AI tasks, graphics rendering, and virtualization.
The price for L40S servers is higher, because of the premium hardware and higher power consumption, but you get more power, optimized for AI workloads.
3. Look at virtualization and cloud deployment
For VDI (Virtual Desktop Infrastructure), cloud gaming, or remote workstation applications, the L40 is sufficient.
For AI-based cloud services, multi-GPU deep learning clusters, or scientific computing in the cloud, go with the L40S.
| Question | L40 | L40S |
|---|---|---|
| Is your workload focused on graphics, visualization, or virtualization? | ✅ | ❌ |
| Do you need basic AI inference but not heavy AI training? | ✅ | ❌ |
| Are you running AI model training, deep learning, or generative AI? | ❌ | ✅ |
| Do you require HPC for scientific computing or financial modeling? | ❌ | ✅ |
| Are power efficiency and cost major concerns? | ✅ | ❌ |
| Do you have the infrastructure for higher power and cooling needs? | ❌ | ✅ |
Getting started with a GPU server
NVIDIA’s L40 and L40S are both great GPU options. The difference really comes down to whether or not you need the extra power offered by the L40S, in very general terms:
- Choose the L40 if your workload involves 3D design, VFX, CAD, virtualization, or basic AI inference.
- Choose the L40S if your workload is AI-heavy, requires deep learning, or involves HPC simulations.
Once you’ve decided which GPU you need, the next step is to choose a GPU server hosting provider, and that’s where Liquid Web comes in. Our team of experts has been providing the best server hardware and hosting solutions for decades, and now we’re applying all that experience to GPU server hosting as well.
If you’re in need of a fast, reliable, and secure platform for ML/DL training models—or another high-performance computing workload—Liquid Web has the options you need. We offer a variety of NVIDIA GPU servers, with different levels of server management. Click below to explore all your options or start a chat with one of our support experts today.
Additional resources
Best GPU server hosting [2025] →
Top 4 GPU hosting providers side-by-side so you can decide which is best for you
What is GPU as a Service? →
What is GPUaaS and how does it compare to other GPU server models?
GPU for AI →
How it works, how to choose, how to get started, and more
Brooke Oates is a Product Manager at Liquid Web, specializing in Cloud VPS and Cloud Metal, with a successful history of IT/hosting and leadership experience. When she’s not perfecting servers, Brooke enjoys gaming and spending time with her kids.