Both the L4 and the L40s (L-series GPUs by NVIDIA) are designed for data centers. Built for completely different purposes, they have the same modern architecture.
It is necessary to explain their particular strengths to help you decide which of these Ada Lovelace GPUs is the correct engine for your intensive AI and visual demands.
What are L4 and L40s GPUs?
Both the NVIDIA L4 and L40s are Tensor Core GPUs. Tensor Cores are specialised processors that greatly speed up matrix operations. These are the basis of deep learning.
They both are equipped with the latest advancements in core technology.
The NVIDIA L4 is great at efficiency.
- It is a single-slot low-profile card designed for maximum density in a server rack.
- The L4 has a very low power consumption of 72 Watt (W), which means that it is highly efficient for inference at scale.
- It mainly focuses on video processing, real-time AI inference, training, graphics, and lightweight cloud services.
NVIDIA L40s offers immense power.
- It is a full-height, dual-slot card designed to be used in the most intensive compute tasks.
- The L40s is a high-performance AI accelerator with professional graphics capabilities.
- Its primary use is training large models, complex simulations, virtual workstations, deep learning training, and high-quality 3D rendering.
The L4 gives great importance to high throughput per watt, hence it is cheap to run at large volumes. The L40s focus on maximum performance to achieve quick time-to-results.
NVIDIA L4 vs. L40s Comparison: AI, ML, and Inference
The L4 and L40s shows largest difference when handling core AI processes, such as training and inference.
The process of prediction using a trained model is called inference. The process of creating the model with the help of data is called training.
The L4 is more competent in the former and the L40s in the latter.
This difference is directly connected to their raw specifications, especially memory and floating-point speed.
Both GPUs have fourth-generation Tensor Cores and third-generation RT Cores.
Shared Architecture: Since they are based on the Ada Lovelace architecture, both of them support advanced data types such as FP8 or FP16 to enable accelerated AI computation.
Inference Efficiency: The L4 is optimized to provide great inference performance per watt. It provides a large number of inference requests per second for its power consumption.
Power Training: L40s has many more CUDA cores and a higher FP32 speed. This enables it to be much more efficient in handling the heavy, iterative calculations needed for ML training and fine-tuning large models.
Memory and Performance for AI
Memory capacity and speed are vital in managing large AI models such as Large Language Models (LLMs).
L4 Memory: The L4 has 24 GB of GDDR6 memory and a memory bandwidth of approximately 300 GB/s. It can be used with medium-sized models or many smaller models running in parallel for inferences.
L40s Memory: The L40s has 48 GB of GDDR6 memory and an 864 GB/s memory bandwidth. This capacity is essential to train large AI models as well as to run massive Generative AI tasks, which consume much memory.
Raw Compute Power: The L40s supports up to 91.6 TFLOPS of FP32 performance. The L4 has 30.3 TFLOPS of FP32. The L40s is over three times faster at single-precision floating-point calculations, which are important for ML training.
NVIDIA L4 vs. L40s: Table of Differences
The table below summarizes the most important technical specifications:
| Feature | NVIDIA L4 | NVIDIA L40s |
| Architecture | Ada Lovelace | Ada Lovelace |
| CUDA Cores | 7,424 | 18,176 |
| GPU Memory | 24 GB GDDR6 | 48 GB GDDR6 with ECC |
| Memory Bandwidth | 300 GB/s | 864 GB/s |
| Peak FP32 Performance | 30.3 TFLOPS | 91.6 TFLOPS |
| Max Power Consumption (TDP) | 72 W | 350 W |
| Primary Workload Focus | Graphics, AI, and video streaming | Inference, Training, Video, Graphics, and Rendering |
| Form Factor | Low-Profile, Single Slot | Full-Height, Dual Slot |
NVIDIA L4 vs. L40s: Visualization and Graphics Workloads
In addition to AI, both GPUs are meant to handle professional graphics. This feature is gaining significance due to the merging of AI and visualization, such as the production of digital twins and virtual worlds, like NVIDIA Omniverse.
L4: Visual Computing Efficiency
L4 is a great GPU for cloud-based visual services with a large number of users sharing the resources. Its focus is on the accessibility and scalability of high-quality graphics.
Video Processing: The L4 excels at accelerating video workloads. It is capable of supporting more than 1000 concurrent AV1 video streams. This, this is suitable for cloud gaming, streaming services, and video analytics at scale.
Cloud Gaming and Virtual Desktops (VDI): The L4 offers an excellent user experience to remote workers and cloud gamers. It consumes a very low amount of power per user. This low power consumption continues to maintain low operation costs.
Real-Time Rendering: The L4 provides good real-time rendering and ray tracing capabilities for its power envelope. It can be used to have immersive virtual workstations and AI-based avatars.
L40s: Most complex and demanding professional tasks
It often serves as the workhorse for creators and engineers.
High-End Rendering: The L40s have significantly higher RT Cores and more raw performance. It is used in professional visualization for tasks such as film rendering, engineering design, and production of photorealistic digital twins.
Generative AI Content Creation: The card has a huge memory of 48GB, and speed makes it essential for Generative AI that can generate high-resolution images, videos, and 3D assets at a rapid pace.
Workload Consolidation: L40s can manage both AI workload and graphics workload-intensive tasks on a single card. This makes infrastructure simple and reduces the need for multiple specialized systems.
Cantech’s NVIDIA L4 and L40s GPU Offerings
Such advanced GPUs need a powerful and scalable server infrastructure. Cantech is a high-performance computing (HPC) solutions provider that ensures you get the full potential of cards such as the L4 and L40s.
Cantech offers unprecedented customization and control of Dedicated HPC Servers.
Complete Hardware Customization: The customers can customize the hardware of the server, including the GPU, processor, memory, and storage. This ensures the infrastructure perfectly matches the workload requirements of the L4 or L40s.
No Virtualization Layer: Cantech provides bare-metal infrastructure. This means that there is no virtualization layer overhead. Users get the full raw power of their dedicated L4 or L40s GPU.
High-End Security and Reliability: Advanced security features are implemented to protect the servers, such as DDoS protection. Tier-III and IV data centers host them and ensure maximum uptime and data integrity.
ML and Generative AI optimized: Their system is optimized to support high-power workloads. This is perfect for running large LLM inference on the L4 or the difficult training jobs on the L40s.
Scalable Resources: Cantech has cost-effective scalability. There is an easy way of upgrading components, such as memory or CPU, as the AI models expand.
Conclusion
The NVIDIA L4 and L40s are both significant Ada Lovelace family data center GPUs. The power efficiency leader for mass-scale inference is the L4. It is the rational option to run countless video streams, VDI sessions, and smaller AI models.
The ultimate universal compute engine is the L40s. It is essential for high-demand tasks such as training large language models, fine-tuning Generative AI, data analytics processing, and powering large 3D graphics. You have to choose according to your main objective. The right GPU will optimize performance and cost.
FAQs
NVIDIA L4 vs. L40s: Which GPU is better for running Large Language Model (LLM) inference?
This decision is based on the size of the LLM and the performance requirement. The L4 performs well in executing many smaller LLM inference tasks at a very low power consumption.
The L40s, which have a larger 48 GB memory, are required in order to run the largest and most advanced LLMs or to run multi-modal AI applications.
What is the benefit of the low power consumption of the L4?
The L4 has a power consumption of only 72W, which is highly beneficial in terms of operation. It enables data centers to accommodate many more GPUs in the same server rack without exceeding the power or cooling limits.
This density minimizes the overall cost of ownership and makes it more energy efficient for large-scale deployments such as video streaming and cloud gaming.
Is the L40s suitable for ML training, or just inference?
L40s is a powerful dual-purpose card. It is very suitable for both ML inference and training. Its FP32 performance, memory bandwidth, and 48 GB of memory are great for use when training small to medium-sized AI models.
It can also fine-tune larger models, providing a more generalized substitute to more specialized training GPUs.
Which graphics card is more appropriate in terms of professional 3D rendering and visualization?
L40s is much more optimized for high-end professional 3D rendering and visualization. It has a higher number of CUDA and RT Cores that offer significantly faster real-time ray tracing and complex simulation.
Although the L4 is capable of supporting simple virtual workstations, the L40s is required when it comes to more advanced work.
