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GPU → A100 vs H100 vs L40S
A100 vs H100 vs L40S: A simple side-by-side and how to decide
Picking the right GPU isn’t just about raw power—it’s about efficiency and scalability as well. Whether you’re training AI, crunching data, or rendering visuals, choosing the wrong one could slow you down in a number of ways.
Let’s look at three of the most popular GPU chips on the market, side-by-side, so you can decide which one you need.
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A100 vs H100 vs L40S overview
Here’s a quick look at how these three NVIDIA GPU chips compare:
| L40S | A100 | H100 | |
|---|---|---|---|
| Architecture | Ada Lovelace | Ampere | Hopper |
| Memory | 48GB GDDR6 | 40GB or 80GB HBM2e | 80GB HBM3 |
| Memory bandwidth | 864 GB/s | Up to 2 TB/s | Up to 3.35 TB/s |
| Tensor cores | 568 (4th Gen) | 432 (3rd Gen) | 528 (4th Gen) |
| Power consumption | 350W | 400W | 700W (SXM) / 350W (PCIe) |
| FP64 performance | Not optimized for FP64 | 9.7 TFLOPS | 60 TFLOPS |
| FP32 performance | 91.6 TFLOPS | 19.5 TFLOPS | 60 TFLOPS |
| TF32 tensor performance | 183.2 TFLOPS | 156 TFLOPS | 989 TFLOPS |
| INT8 performance | 734.4 TOPS | 1,248 TOPS | 3,956 TOPS |
| NVLink support | No | Yes (600 GB/s) | Yes (900 GB/s) |
| PCIe Gen | Gen 4 | Gen 4 | Gen 5 |
| Form factor | Dual-slot PCIe | PCIe & SXM | PCIe & SXM |
| Relative cost | $$ (Lower cost) | $$$ (Mid-range) | $$$$ (Highest cost) |
We’ll get into details on each, but based on specs and design, here’s a quick TL;DR on which is best for some common GPU use cases:
| Use case | Best GPU choice | Reason |
|---|---|---|
| AI/Deep learning training | H100 | Offers the highest TF32 and INT8 tensor performance, along with NVLink for multi-GPU scaling, making it ideal for large-scale AI training |
| AI/Deep learning inference | A100 (for cost-efficiency) or H100 (for max performance) | A100 provides good inference performance with lower power consumption, while H100 excels for extreme workloads requiring the highest throughput. |
| High-performance computing (HPC) | H100 | Delivers the exceptional FP64 performance and memory bandwidth, important for scientific computing and simulations |
| Data analytics and big data processing | A100 | High memory bandwidth and tensor cores are well-suited for large-scale data processing. |
| Cloud computing and virtualization | A100 | Optimized for multi-tenant cloud environments, with excellent memory bandwidth and virtualization support |
| Rendering and visual effects | L40S | Optimized for graphics and rendering workloads, with high FP32 performance and large GDDR6 memory |
| Media and video processing | L40S | Strong encoding/decoding capabilities and efficient power usage for media workloads |
| Gaming and real-time graphics | L40S | Provides superior real-time rendering performance |
| Edge AI and AI workloads on limited power budgets | A100 | Balanced performance with lower power draw compared to the H100, making it ideal for power-sensitive environments. |
NVIDIA A100: The versatile workhorse
The A100 is a balanced choice for businesses that need a mix of AI, data analytics, and cloud computing power without the highest costs. Released in May 2020, it provides strong performance for machine learning, big data processing, and virtualized workloads, which makes it a go-to option for AI startups, cloud providers, and research institutions.
- Best for: Moderate AI model training and inference, cloud computing, and large-scale data analytics.
- Why choose it? A strong mix of performance and efficiency, with excellent scalability for multi-GPU setups.
- Relative cost: Mid-range – powerful but not the most expensive.
NVIDIA H100: The AI and supercomputing powerhouse
The H100 is NVIDIA’s most advanced AI chip, built for organizations that need cutting-edge AI model training, deep learning, and scientific computing at massive scale. It was released at the end of 2022 and offers the highest speed and efficiency for AI workloads. There is, of course, a higher price and power consumption that come with that performance.
The H100 is ideal for enterprise organizations that are pushing the boundaries of AI and high-performance computing (HPC).
- Best for: Large-scale AI training, advanced scientific research, and high-performance computing.
- Why choose it? Maximum performance and efficiency for next-gen AI, if you can make the infrastructure investment.
- Relative cost: Highest – premium pricing for top-tier performance.
NVIDIA L40S: The graphics and AI hybrid
The L40S is designed for graphics-heavy workloads like 3D rendering, media production, and digital twins, while also offering solid AI processing capabilities. It was released at the end of 2023 as a great fit for businesses in visual effects, architecture, and content creation that also want some AI processing power without over-investing in a dedicated AI chip.
- Best for: Rendering, media production, and AI-augmented design applications.
- Why choose it? Strong in real-time graphics, visualization, and AI acceleration for creative workflows.
- Relative cost: More affordable than the A100 and H100.
How to choose a GPU server
So which GPU, or GPU server, do you need? Here are four key considerations to help you decide.
1. Workload requirements
The most important factor is what you need the GPU for. Here are a few popular use cases:
- AI and Deep Learning training → H100 (Best for large-scale AI training, but costly)
- AI inference and cloud AI services → A100 (Efficient for running AI models at scale)
- Rendering and graphics processing → L40S (Optimized for 3D workloads and content creation)
- High-performance computing (HPC) → H100 (Best for scientific computing and simulations)
- General-purpose machine learning and data processing → A100 (Good mix of power and affordability)
2. Cost and budget considerations
Unfortunately, budgets have to be consulted. Since renting a GPU server is a recurring expense, businesses need to balance performance vs. cost:
- H100 servers are the most expensive but deliver top-tier AI performance.
- A100 servers offer strong performance at a lower cost and are widely available.
- L40S servers are the most affordable.
3. Scalability and deployment flexibility
Think about how much GPU power you need and how consistent that need really is.
- If a company needs multiple GPUs for parallel processing, renting H100 or A100 with NVLink support is ideal.
- If a company needs on-demand AI processing without continuous high usage, A100 cloud instances provide a flexible, cost-effective option.
- If the workload is rendering-heavy but doesn’t require AI, L40S offers the best price-to-performance ratio.
4. Availability and hosting provider offerings
If you’re already working with a hosting provider, or you have one in mind, make sure they offer the GPU you prefer.
- Not all hosting providers offer the H100 yet, as it is newer and more expensive.
- A100 is widely available and often comes with different memory configurations (40GB or 80GB).
- L40S is a newer alternative, and businesses renting it should ensure it meets their needs for AI and graphics workloads.
Getting started with a GPU server
NVIDIA offers a range of GPU chips, but they were each designed to meet a specific need. The H100 is a new, improved version of the A100, and the L40S (an upgrade from the L40) was designed to make GPU power more accessible.
Start by outlining your specific GPU needs. Investigate what kind of compute power you need, how consistently you need it, and where you expect those needs to be in six to 12 months.
When you’re ready for the best GPU server hosting available, Liquid Web can help. We offer GPU servers with L40S and H100 chips. On top of the premium hardware, you get the best uptime guarantees, server security, and customer support in the industry.
Click below to explore GPU server hosting options or start a chat right now to talk to one of our team members.
Additional resources
Best GPU server hosting [2025] →
Top 4 GPU hosting providers side-by-side so you can decide which is best for you
NVIDIA L40 vs L40S →
A side-by-side comparison of the L40 and L40S chips, so you can decide which is right for you.
GPU for AI →
How it works, how to choose, how to get started, and more
Amy Moruzzi is a Systems Engineer at Liquid Web with years of experience maintaining large fleets of servers in a wide variety of areas—including system management, deployment, maintenance, clustering, virtualization, and application level support. She specializes in Linux, but has experience working across the entire stack. Amy also enjoys creating software and tools to automate processes and make customers’ lives easier.