Artificial Intelligence (AI), particularly Generative AI (or Gen AI), is making significant in-roads in every aspect of business, enabling businesses to improve efficiency and elevate customer experience. As per reports, the market size of AI is expected to show an annual growth rate of 27.67 percent, resulting in a market volume of US$826.70 billion by 2030.
The release of NVIDIA’s GeForce RTX 50 Series desktop, laptop GPUs, and AI Supercomputer were some of the major talking points of CES 2025! In 2025, more startups, enterprises, as well as individual developers are set to embrace AI; with Agentic AI touted to be the next big thing in the tech industry.
At the helm of any complex AI workload is the GPU – the backbone of computations within the AI workflow. Since NVIDIA offers a wide range of GPUs with varied capabilities (and configurations), it becomes crucial to select the one that aligns with your workflow requirements. In this blog, we delve into the nuances of three popular GPUs – L40S, H100, A100 and compare them from various perspectives.
NVIDIA GPU Range
As stated earlier, NVIDIA offers several top-tier GPU’s that can be leveraged for varied use cases such as gaming, video editing/streaming, handling computationally intensive AI/ML workloads, amongst others. The best-suited GPU can be chosen based on the architecture, performance metrics, power efficiency, and other features that meet the demands of your workload & HPC (High-Performing Computing) applications.
Shown below is a pictorial representation of the high-level comparison of the L40S, H100, and A100 GPUs.
Here is a short gist of how the NVIDIA A100, H100, and L40S stack up against each other:
NVIDIA A100 Tensor Core GPU
It is based on the Ampere architecture and features 54 billion transistors. The NVIDIA A100 is available in the following memory configurations:
- 40GB PCIe & 80GB PCIe
- 40GB SXM & 80GB SXM
NVIDIA A100 can provide up to 20x higher performance than the prior NVIDIA Volta generation. The A100 GPU can cater to a wide array of tasks, including those in healthcare and finance, through its Multi-Instance GPU (MIG) capability.
NVIDIA H100 Tensor Core GPU
The NVIDIA H100 is based on the NVIDIA Hopper architecture, owing to which it is able to speed up large language models by 30x. Since this category of GPU features fourth-generation Tensor Cores and a Transformer Engine (FP8 precision), it is able to train models 4x faster than the prior generation for GPT-3 (175B) models.
H100 has the potential to securely accelerate all workloads for every data center, from enterprise to exascale. As stated in the H100 datasheet, this family of GPUs offers up to 30 times better performance in running inference tasks for the largest AI models in comparison to the previous generation of hardware or baseline systems.
NVIDIA L40S
Unlike A100 & H100, the NVIDIA L40S GPU is built on the Ada Lovelace architecture. It is considered to be the most powerful universal GPU for the data center, capable of delivering E2E acceleration for the next generation of AI-enabled applications.
The fourth-gen Tensor cores in L40S provide performance gains for faster AI & data science training. On the other hand, third-gen Ray Tracing (RT) cores in the L40S offer massive improvements in the ray-tracing performance.
Though NVIDIA offers a wide range of GPUs that cater to a plethora of use cases, our focus will be limited to the three GPUs discussed so far in the blog. In the subsequent sections of this blog, we will be comparing these GPUs on the basis of different parameters – overall capabilities, performance benchmarks, power consumption, amongst others!
Features of A100, H100, and L40S
All three GPUs are top-notch options for processing AI/ML workloads, High Performance Computing (HPC), and more. However, let’s look at some of the salient features of the GPUs under discussion:
Architecture and Specifications
The NVIDIA A100 built on the Ampere architecture, consists of 54 billion transistors in comparison to its predecessor V100’s 21.1 billion transistors. The NVIDIA H100, built on the Hopper Architecture, is packed with 80 billion transistors. The NVIDIA L40S is built on the Ada Lovelace Architecture and constitutes 35.8 billion transistors.
The advanced design of H100 is instrumental in providing much better performance metrics, up to 4X higher AI training on GPT-3 when compared to the NVIDIA A100 GPU.
The L40S is the most powerful GPU universal GPU from NVIDIA. It is suitable for LLM-fine tuning, small model training, and video streaming applications.
Tensor Cores
The third-generation tensor cores in the NVIDIA A100 provide 2x performance boost for sparse models. This not only benefits AI inference but also largely improves the performance of model training. It delivers 312 teraFLOPS (TFLOPS) of deep learning performance.
These cores are capable of delivering optimal performance across AI and HPC tasks due to support of precisions like FP64, FP32, TF32, BF16, INT8, and more. The structural sparsity support in A100 helps in processing more data in less time, thereby speeding up training and inference times for AI models.
In comparison to the A100, the NVIDIA H100 features fourth-generation tensor cores and a Transformer Engine with FP8 precision. This results in 4x faster training over the prior generation for the GPT-3 (175B) model. The Tensor Memory Accelerator (TMA), which is a new part of the Hopper Architecture, frees up the GPU from memory management tasks.
The TMA has the capability to transfer large chunks of data between global memory and shared memory. The addition of TMA makes H100 a suitable choice for large-scale AI-model training.
Akin to the H100, the NVIDIA L40S also features the fourth-generation tensor cores due to which it offers out-of-the-box performance gains for faster AI and data science model training. The L40S can achieve up to 1.2x greater inference performance when running Stable Diffusion than compared to the A100. This gain can be attributed to its Ada Lovelace Tensor Cores and FP8 precision.
Recommended Blog: CUDA cores vs Tensor cores: Choosing the Right GPU for Machine Learning
Lastly, DLSS (Deep Learning Super Sampling) in L40S helps accelerate AI-enhanced graphics capabilities by upscaling resolution in certain applications.
Memory Variants
The NVIDIA A100 is available in two variants – 40GB and 80GB of memory. The 80GB HBM2e (High Bandwidth Memory) variant is capable of running simulations in around 4 hours and delivering fastest GPU memory bandwidth of over 2TB/s.
Unlike the A100, the NVIDIA H100 is also available in two memory variants – 80GB (HBM2e) and 94GB (HBM3) memory. It is known for higher Thermal Design Power (TDP) of up to 700W which essentially means that it targets more demanding applications at the cost of energy efficiency. In case your priority is improved power efficiency, it is recommended to opt for H100 over A100.
Contrast to A100 & H100, the NVIDIA L40S is available only with 48GB of GDDR6 memory. The variant is specifically designed for handling complex graphics-intensive applications, complex AI models, large datasets, and more.
Multi-Instance GPU (MIG)
For starters, Multi-Instance GPU (MIG) technology lets you partition a single GPU into a number of completely isolated instances. Hence, each GPU instance operates independently and efficiently, with dedicated resources like CUDA cores, compute cores, memory, Cache, etc.
MIG is more effective in multi-tenant environments like data centers, edge devices, etc. as users can share the same physical GPU in an isolated manner. Since resources are managed and utilized more effectively with MIG, it has a monumental impact on power consumption!
A100 and H100 both support the MIG technology. However, MIG is disabled by default and needs to be configured by the user/administrator. The A100 was the very first GPU by NVIDIA with the MIG support. On NVIDIA A100, you can spin up to 7 independent instances per GPU.
- A100 (40GB PCIe, 40GB SXM) – Up to 7 MIGs @ 5GB
- A100 (80GB PCIe, 80GB SXM) – Up to 7 MIGs @ 10GB
Similar to A100, you can also spin up to 7 MIGs with the NVIDIA H100.
- H100 SXM – Up to 7 MIGs @ 10GB
- A100 NVL – Up to 7 MIGs @ 12GB
While A100 with MIG is more suited for usage in data centers, H100 with MIG is ideal for AI/ML training and inference, HPC, and other workloads with higher computational requirements. MIG is not supported on the NVIDIA L40S.
Apart from the above-mentioned pointers, you could also evaluate the GPUs on the basis of CUDA cores, fault detection, integration with cloud-based infrastructure, and more.
Performance and Use Cases
Since all the three GPUs cater to different use cases, performance becomes a more subjective matter in this discussion. Depending on the scope and scale of the workload, all these GPUs offer unique benefits for AI and ML applications.
High-Performance Computing (HPC)
The NVIDIA A100 is the ideal choice for handling HPC workloads in fields such as science and engineering, amongst others. It can handle traditional simulations and AI-accelerated computations in an efficient manner. Researchers can leverage the potential of A100 to deliver real-world results and deploy solutions into production at scale.
A100, which is available for desktops to servers to cloud services, helps in accelerating over 2,000 applications, including every major deep learning framework.
Though A100 is ideal for handling complex simulations, the NVIDIA H100 is more suited for advanced HPC applications. This is primarily due to H100’s capability to triple the floating-point operations per second (FLOPS) of double-precision Tensor Cores, thereby delivering 60 teraflops of FP64 computing for HPC. The NVIDIA L40S is not suited for high-performance computing applications.
On the whole, A100 is the best suited for HPC for data centers and cloud environments due to its higher performance and cost effectiveness.
AI Inference
The NVIDIA A100 is capable of delivering unparalleled performance for deploying AI models in production. It can handle a mix of workloads related to NLP, computer vision, matrix multiplications, etc. with utmost ease. The fine-grained structured sparsity in A100 Tensor Cores largely benefits AI Inference, as it helps improve the performance of model training.
Built on the Hopper Architecture, the NVIDIA H100 is designed to accelerate tasks related to AI training and inference. Hopper Tensor Cores can apply mixed FP8 and FP16 precisions that aid in accelerating AI calculations for transformers.
As stated in the H100 datasheet, fourth-generation Tensor Cores and Transformer Engine with FP8 precision provide up to 4X faster training over the prior generation for GPT-3 (175B) models. The Transformer Engine in H100 has the potential to optimize the performance of transformer models that are extensively used in AI inference tasks (e.g. NLP, machine translation, and large-scale language models).
The NVIDIA L40S is ideal for edge AI inference and workloads that require AI-powered media processing with inference tasks. If your workload involves both AI & graphics processing, you should opt for L40S since it enhances rendering with DLSS 3. It boosts graphical performance and delivers superior visual quality with reduced latency.
Though all the three GPUs can handle AI Inference workloads very efficiently, each of them can be leveraged for different varieties of workloads. As seen so far, the NVIDIA H100 should be your go-to choice if performance of large-scale models is your priority. On the other hand, NVIDIA L40S is best-suited for AI Inference workloads with extremely low latency (e.g. real-time video analytics, media processing, etc.).
Graphics Rendering and Video Applications
The NVIDIA A100 can help achieve flawless input video decoding, training, and inference performance. The third-generation Tensor Cores in the A100 can accelerate tasks related to AI-enhanced graphics, such as upscaling, denoising, and ray tracing. The MIG support also plays a pivotal role in achieving high throughput whilst reducing power consumption.
Though A100 can handle media-related tasks efficiently, it is not the best choice for real-time graphics rendering tasks. The NVIDIA H100 is a powerful GPU that offers excellent performance for ray tracing (RT) and high-end simulations for virtual production. However, it is an expensive proposition for handling less demanding video rendering or graphics tasks.
The NVIDIA L40S is a clear winner in this category, as the Ada Lovelace architecture is tailored for graphics rendering and video applications. With 48 GB of GDDR6 memory, even the most intense graphics applications run with the highest level of performance.
The L40S provides the computational and graphical power that are necessary to render complex visuals in real-time. Third-generation Ray Tracing (RT) Cores (142 in total) and 48GB memory capacity helps deliver up to twice the real-time ray-tracing performance of the A100 and H100.
Game developers and VFX artists who need a GPU capable of handling intricate scenes with top-notch detail should opt for L40S over A100 & H100.
What we have covered so far are the most prominent use cases that can be realized with A100, H100, and L40S.
A100 vs. H100 vs. L40S Comparison
As seen so far, the A100, H100, and L40S are each designed for realizing specific workloads and use cases. While A100 should be your choice if your use case is related to AI training and Inference. On the other hand, H100 can handle cutting-edge AI workloads and advanced HPC super efficiently!
The energy-efficient design of the L40S is perfect for environments where efficiency and cost are top priorities (e.g. AI inferencing in recommendation systems, cloud-based VMs, or distributed machine learning models).
Here is a technical comparison of A100, H100, and LS40:
| Specification | NVIDIA A100 | NVIDIA H100 | NVIDIA L40S |
| Memory | 40GB HBM2
80GB HBM2/PCIe | 80GB | 48GB (GDDR6) |
| Memory Bandwidth | 1.6 TB/s | 2 TB/s | 864GB/s |
| Architecture/Series | Ampere | Hopper | Ada Lovelace |
| CUDA Core | 6,912 | 14,592 | 18,176 |
| Tensor Core | 432 third-generation | 640 third-generation | 568 fourth-generation |
| RT Core | N/A | 128 | 142 |
| MIG | Up to 7 MIGs @ 5GB/10GB | Up to 7 MIGs @ 10GB/12GB | N/A |
| FP32(single precision) | 19.5 teraFLOPS | 51 teraFLOPS | 91.6 teraFLOPS |
| FP16(half precision) | 312/624 teraFLOPS | 1513 teraFLOPS | 733 teraFLOPS |
| TF32 (TensorFloat) | 156/312 teraFLOPS | 756 teraFLOPS | 366 teraFLOPS |
| Max thermal design power (TDP) | 250W (40GB PCIe) 300W (80GB PCIe)400W (40GB & 80GB SXM) | Up to 700W (H100 SXM)
Up to 350~400W (H100 NVL) | Passive (Up to 350W) |
| Form Factor | 2-slot, full height, full length (FHFL) | SXM or 2 slot PCIe, full height | 4.4” H x 10.5” L, Dual Slot |
Which GPU suits your requirements?
There is no one size fits all approach that can be applied when choosing the ideal GPU. Here are some general pointers that can help you make at an informed decision:
- NVIDIA A100: Scientific computing, AI Training, Machine Learning, Data Analytics, & more
- NVIDIA H100: Healthcare Imaging, Autonomous Vehicles, Financial Trading, Game Development, Cybersecurity, & more
- NVIDIA L40S: 3D Graphics and Rendering, Computer Vision, Scientific Simulations, Multi-Workload Environments, & more
The choice largely depends on your use case, preferences, and budget!
It’s A Wrap
In this blog, we deep dived into the nuances of NVIDIA’s cutting-edge GPUs – the A100, H100, and L40S – primarily designed for professional, enterprise, and data center applications. Irrespective of the GPUs you choose, an on-premise setup can put brakes on both scalability and reliability. Not to mention, it can lead to long-term costs, as the GPU setup would need timely updates & maintenance.
This is where you could opt for a GPUaaS (GPU as a Service) provider like AceCloud. You can host A100, H100, and L40S in their secure, reliable, and economical cloud environment.
Book a free consultation with an AceCloud expert today.
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