Artificial Intelligence (AI), particularly Generative AI (or Gen AI), is quickly enabling businesses to improve efficiency and elevate customer experience. As per Statista, 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.
In 2026, more startups, enterprises, as well as individual developers are set to embrace. No wonder Agentic AI is touted to be the next big thing in the tech industry. However, at the helm of any complex AI workload is 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. Here, we will delve into the nuances of three popular NVIDIA GPUs: L40S, H100, A100 while comparing them from various perspectives.
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 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 NVIDIA 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.
Comparing Features of A100, H100 and L40S
All three NVIDIA 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. 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.
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.
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.
Also Read: 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 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 and more.
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 three NVIDIA 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 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.
- 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. The Transformer Engine in H100 has the potential to optimize the performance of transformer models that are extensively used in AI inference tasks.
- 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.
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.
- 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.
Technical Summary: A100 vs. H100 vs. L40S Comparison
NVIDIA 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).
| Specification | NVIDIA A100 | NVIDIA H100 | NVIDIA L40S |
|---|---|---|---|
| Memory | 40GB HBM280GB HBM2/PCIe | 80GB | 48GB (GDDR6) |
| Memory Bandwidth | Up to 1.6 TB/s | Up to 2 TB/s | Up to 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 NVIDIA 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
As we have mentioned before, the choice of GPU largely depends on your use case, preferences and budget.
Choose AceCloud for Your Next AI Workload
At the end of the day, the “best” GPU is the one that meets your goals and shows up when you need it. Whether you pick L40S for graphics and efficient inference, A100 for proven training, or H100 for maximum GenAI performance, AceCloud makes it simple to get started fast.
Spin up NVIDIA GPU instances in minutes, scale up or down with Pay-as-You-Go options and lean on 24/7 experts plus free migration help when you are moving from another cloud. Check our transparent pricing, then choose confidently. Ready to test? Book a free consultation with an AceCloud expert today!
Frequently Asked Questions
If you’re training large models or scaling across multiple GPUs, H100 is usually the top pick. A100 is a proven training workhorse and can be a strong value. L40S is better suited to lighter training and fine-tuning.
For high-throughput, production inference, H100 often wins. A100 is a reliable choice for steady serving. L40S is great when you want efficient inference plus graphics or media features.
No. MIG and NVLink are typically associated with A100 and H100, which makes them better for multi-tenant isolation and multi-GPU training at scale.
The same GPU name can come in different forms. SXM/NVL variants are often designed for higher bandwidth and better multi-GPU scaling, while PCIe is common for general server deployments and many inference setups.
VRAM needs depend on model size, precision (FP16/BF16 vs INT8/4-bit) and context length (KV cache). If you’re unsure, start with your model + expected context, then size up for headroom and batching.
Yes, but it depends on the GPU and your isolation needs. With MIG-capable GPUs (like A100/H100), you can split a GPU into smaller slices for multiple jobs. Otherwise, you’ll typically rely on software-level scheduling and careful resource limits.
Because you can get NVIDIA GPUs fast without the usual hassle: spin up the GPU you need, scale on demand, use spot options when cost matters and lean on migration help and support when you’re moving workloads or deploying at production scale.
