
The GeForce RTX 4090 and the A100 both sit at the high end of NVIDIA's GPU lineup, but they're built for very different worlds.

The **RTX 4090** is a powerful consumer-grade GPU, designed for ultra high-performance gaming, professional creative work, and even some entry-level AI workloads. It's surprisingly capable across a variety of tasks, especially for its price point.

The **A100**, on the other hand, isn't aimed at consumers at all. It's an enterprise-grade GPU built for data centers, research labs, and AI teams running large-scale model training, inference, and simulation workloads – designed to move serious data at serious speed.

In this post, we'll look at how these two machines compare with each other, and why you might choose one over the other based on your specific needs.

### NVIDIA GeForce RTX 4090: A Workhorse with Range

When it launched in September 2022 as the top-tier consumer GPU of its generation, the RTX 4090 quickly became the go-to choice for 4K gaming, 3D rendering, and AI-enhanced creative workflows.

Built on NVIDIA's **Ada Lovelace** architecture and powered by the **AD102 chip**, the RTX 4090 introduced significant upgrades in ray tracing and Tensor Core acceleration, and delivered notable performance gains with **DLSS 3** in a variety of games.

While it's no longer the flagship GPU of NVIDIA's GeForce line (that title now goes to the RTX 5090, which you can read about [here](https://vast.ai/article/nvidia-geforce-rtx-5090-specs-everything-you-need-to-know)), the RTX 4090 definitely still holds its own. It remains one of the most capable and well-rounded GPUs available at its price point for consumers and technical teams alike.

#### Some of the RTX 4090's key specs and features include:

-   **24 GB of GDDR6X memory** with **1,008 GB/s bandwidth** on a **384-bit** interface.  

-   **16,384 CUDA cores** for high-parallel throughput.  

-   **128 third-gen RT cores** and **512 fourth-gen Tensor cores** for advanced ray tracing and AI workloads.  

-   **DLSS 3** support for real-time upscaling and performance boosts in many games.  

-   **450 watts** of thermal design power (TDP), requiring an **850-watt power supply** due to its high processing capabilities.

If you're looking for serious performance in a desktop form factor, the RTX 4090 continues to deliver. It may not be the newest card on the market, but it remains a workhorse that gets the job done across a wide range of applications.

As capable as the RTX 4090 is, it's not built for everything. If your workloads go beyond what a high-end consumer GPU can handle, the A100 just might bring the compute power you need.

### NVIDIA A100: A Powerhouse with Purpose

The A100 is built for scale. Based on NVIDIA's Ampere architecture and powered by the **GA100 chip**, it's designed to tackle massive workloads – like foundational AI model training – thanks to its raw throughput, high-bandwidth memory, and ability to scale across multi-GPU environments.

When the A100 launched in June 2021, it introduced **third-gen Tensor Cores**, support for **TF32** precision, and **Multi-Instance GPU** (MIG) capabilities – enabling flexible resource partitioning and efficient parallel processing across shared environments.

While some of its core specs might look modest compared to the RTX 4090, the A100 delivers superior performance in AI and data-intensive workloads because it's optimized where it counts: memory bandwidth, interconnects, and specialized compute.

For instance, the A100's memory clock is much lower than the RTX 4090's on paper (roughly 3 Gbps vs. 21 Gbps). However, the A100 uses **HBM2e memory** with a much wider **5,120-bit interface**. This design allows it to deliver around **2 TB/s** of bandwidth – double the RTX 4090 – despite the lower frequency. It's an approach that prioritizes efficiency and scale.

#### To recap, here are a few notable features of the A100:

-   **80 GB of HBM2e memory** with about **2 TB/s of bandwidth** (twice that of the RTX 4090).  

-   **5,120-bit memory bus**, an exceptionally wide interface enabling fast access to memory, reducing bottlenecks in AI and high-performance computing (HPC) workloads.  

-   **432 third-gen Tensor Cores**, with high-throughput **TF32** performance for AI and robust **FP64** support for HPC workloads.  

-   **Multi-Instance GPU (MIG)** support, so it can be partitioned into up to 7 isolated GPU instances – ideal for multi-tenant workloads and fine-grained resource allocation.  

-   **NVLink and NVSwitch** allow up to 16 A100 GPUs to be interconnected at up to **600 GB/s** of bi-directional bandwidth per GPU.

In short, the A100 delivers serious performance for large-scale AI and HPC workloads. But if you're comparing it head-to-head with the RTX 4090 on specs alone, here's how the two stack up.

### RTX 4090 vs. A100: Key Specs Compared

For reference, this table highlights some of the features and specs of the RTX 4090 and A100:

<table class="w-full border-collapse border border-gray-300">
    <thead>
        <tr class="bg-gray-100">
            <th class="border border-gray-300 px-4 py-2 text-left font-semibold">
                Specification
            </th>
            <th class="border border-gray-300 px-4 py-2 text-center font-semibold">
                RTX 4090
            </th>
            <th class="border border-gray-300 px-4 py-2 text-left font-semibold">
                A100
            </th>
        </tr>
    </thead>
    <tbody>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Architecture
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                Ada Lovelace
            </td>
            <td class="border border-gray-300 px-4 py-2">Ampere</td>
        </tr>
        <tr class="bg-gray-50">
            <td class="border border-gray-300 px-4 py-2 font-semibold">VRAM</td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                24 GB GDDR6X
            </td>
            <td class="border border-gray-300 px-4 py-2">80 GB HBM2e</td>
        </tr>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Memory Clock
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                1313 MHz 21 Gbps
            </td>
            <td class="border border-gray-300 px-4 py-2">1512 MHz 3 Gbps</td>
        </tr>
        <tr class="bg-gray-50">
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Memory Bus Width
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">384</td>
            <td class="border border-gray-300 px-4 py-2">5120</td>
        </tr>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Bandwidth
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                1.01 TB/s
            </td>
            <td class="border border-gray-300 px-4 py-2">1.94 TB/s</td>
        </tr>
        <tr class="bg-gray-50">
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Streaming Multiprocessors (SMs)
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">128</td>
            <td class="border border-gray-300 px-4 py-2">108</td>
        </tr>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Tensor Cores
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">512</td>
            <td class="border border-gray-300 px-4 py-2">432</td>
        </tr>
        <tr class="bg-gray-50">
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                CUDA Cores
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">16,384</td>
            <td class="border border-gray-300 px-4 py-2">6,912</td>
        </tr>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Ray Tracing Cores
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">128</td>
            <td class="border border-gray-300 px-4 py-2">N/A</td>
        </tr>
        <tr class="bg-gray-50">
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Base Clock
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                2235 MHz
            </td>
            <td class="border border-gray-300 px-4 py-2">1065 MHz</td>
        </tr>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Boost Clock
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                2520 MHz
            </td>
            <td class="border border-gray-300 px-4 py-2">1410 MHz</td>
        </tr>
        <tr class="bg-gray-50">
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Multi-Instance GPUs
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                No MIG Support
            </td>
            <td class="border border-gray-300 px-4 py-2">
                Up to 7 MIGs @ 10GB each
            </td>
        </tr>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Thermal Design Power (TDP)
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">450W</td>
            <td class="border border-gray-300 px-4 py-2">300W</td>
        </tr>
        <tr class="bg-gray-50">
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Recommended Power Supply
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">850W</td>
            <td class="border border-gray-300 px-4 py-2">700W</td>
        </tr>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Launch Date
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                Sept. 20, 2022
            </td>
            <td class="border border-gray-300 px-4 py-2">June 28, 2021</td>
        </tr>
    </tbody>
</table>

As mentioned, specs are just one part of the equation. Ultimately, choosing the right GPU depends on your workload, environment, and priorities.

### Use Cases: Which GPU Should You Choose?

Here’s how the strengths of each GPU align with different types of use cases.

#### You should consider the RTX 4090 if you prefer:

-   **A powerful, desktop-friendly GPU** for high-end gaming, creative workflows, and light AI experimentation.
-   **Excellent performance-per-dollar in single-GPU setups**, particularly for developers and small teams.
-   **A widely available, less costly option** for rendering, video editing, or running small to medium AI models locally.

#### Whereas the A100 may be a better fit if you need:

-   **Scalable performance** for training large models, running high-throughput inference, or powering complex simulations.
-   **A data center-optimized GPU** with advanced memory architecture and precision modes tailored for AI and HPC workloads.
-   **Flexible deployment options**, including support for multi-instance workloads and high-speed interconnects in clustered environments.

\------------------------------

At **Vast.ai**, we understand there are other considerations, as well. Access, flexibility, and affordability are just as important, especially when you're experimenting, scaling, or moving fast.

That's why our market-based cloud GPU rental platform gives you affordable, on-demand access to RTX 4090s, A100s, and much more. With Vast.ai, you can instantly match the right hardware to your workload – without the upfront investment and overhead of managing your own infrastructure.

Try Vast today and discover what you can achieve with compute power that fits your needs and your budget\!


Compare NVIDIA’s RTX 4090 vs A100 GPUs to find out which is better for gaming, AI, HPC, or creative work—and how to access both on Vast.ai.

NVIDIA RTX 4090 vs. A100: Two Powerhouses, Two Purposes


# Choosing the Right GPU for Your Needs

Choosing the GPU that best suits your needs can be challenging. Whether you're tackling AI and graphics-intensive tasks, focusing on universal computing at the data center level, or pushing the boundaries with exascale high-performance computing (HPC), it's crucial to understand the differences between various GPUs and how they might work for you.

Today, we're looking at two of NVIDIA's top-tier GPUs: the versatile **L40S** and the powerhouse **H100**. The L40S shines as a flexible choice, offering robust performance across AI and graphics workloads. Meanwhile, the NVIDIA H100 is the go-to GPU for those looking to achieve unparalleled power in the most demanding AI and HPC environments.

## What are the main differences between the L40S and the H100? Which one is right for your needs? Let's explore these questions and more…

---

## NVIDIA H100: The AI Titan

The **H100** GPU made a big splash when it was released in March 2023. Major names in generative AI like OpenAI, Meta, and Stability AI adopted the H100 to accelerate their cutting-edge work. It's one of the very best GPUs on the market today – particularly well suited for AI and deep learning applications.

Built on the Hopper architecture, the H100 features fourth-gen Tensor Cores and a dedicated Transformer Engine to solve trillion-parameter language models. According to NVIDIA, it can speed up large language models (LLMs) by 30X over its predecessor, the A100 GPU. (We compared the H100 and A100 in a previous blog post [here](https://vast.ai/article/H100-vs-A100-Comparing-two-Powerhouse-GPUs).)

### A few of the H100's standout features include:

-   **Superior Tensor Core throughput performance**, along with advanced Transformer Engine and FP64 and FP8 capabilities for next-gen AI workloads.
-   **Efficient scalability** thanks to fourth-gen NVLink (with 900 GB/s of GPU-to-GPU interconnect), NDR Quantum-2 InfiniBand networking, and PCIe Gen5.
-   **GPU memory bandwidth** of 3.35 TB/s to handle the most data-intensive tasks.
-   **Multi-Instance GPU (MIG) capability**, with superior resource allocation and flexibility in multi-tenant environments or when running multiple workloads at the same time.
-   **Highly efficient sparsity support** that doubles the performance for certain AI and machine learning tasks.

But the H100 does come with a price tag to match its game-changing capabilities – it's an investment for those who need the speed and power it delivers. For instance, if your projects involve massive datasets, large-scale simulations, or advanced AI training for foundational models, the H100 is certainly worth the consideration.

---

## NVIDIA L40S: The Versatile Contender

The **L40S**, while not as specialized as the H100, excels in environments where flexibility is key. Built on the Ada Lovelace architecture, the L40S is designed to handle a wide range of workloads, making it a solid choice for everything from AI training to real-time graphics rendering.

Although it lacks FP64 performance, the L40S compensates with excellent FP32, mixed-precision performance, and Tensor Core capabilities. It also features advanced DLSS 3.0 and ray-tracing, plus DisplayPort outputs and NVENC / NVDEC with AV1 support, making it ideal for graphics-heavy applications.

Its 48MB GDDR6 memory isn't quite comparable to the H100's 80MB HBM3 memory, but it's perfectly respectable nonetheless. Also, its lower memory bandwidth of 864 GB/s (versus the H100's 3.35 TB/s) is something to consider if you're dealing with memory-intensive machine learning scenarios.

### A point in favor of the L40S:

-   It can be used as an Omniverse server and generates highly realistic 3D synthetic data for the most demanding workloads.
-   Ideal for extended (XR) and virtual reality (VR) applications, design collaboration, and digital twins.

The L40S doesn't provide MIG support like the H100 does, but its strength lies in its ability to adapt to a variety of tasks, making it a strong all-around GPU for general computing, AI, and graphics workloads.

---

## Which GPU Should You Choose?

For reference, here is a quick comparison of some of the features and specs of the L40S and H100 GPUs:

<table class="w-full border-collapse border border-gray-300">
    <thead>
        <tr class="bg-gray-100">
            <th class="border border-gray-300 px-4 py-2 text-left font-semibold">
                Feature
            </th>
            <th class="border border-gray-300 px-4 py-2 text-center font-semibold">
                L40S
            </th>
            <th class="border border-gray-300 px-4 py-2 text-center font-semibold">
                H100
            </th>
        </tr>
    </thead>
    <tbody>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                GPU Architecture
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                Ada Lovelace
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">Hopper</td>
        </tr>
        <tr class="bg-gray-50">
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                GPU Memory
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                48 GB GDDR6
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                80 MB HBM3
            </td>
        </tr>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                GPU Memory Bandwidth
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                864 GB/s
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                3.35 TB/s
            </td>
        </tr>
        <tr class="bg-gray-50">
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                CUDA Cores
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">18,176</td>
            <td class="border border-gray-300 px-4 py-2 text-center">14,592</td>
        </tr>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                FP64 TFLOPS
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">N/A</td>
            <td class="border border-gray-300 px-4 py-2 text-center">33.5</td>
        </tr>
        <tr class="bg-gray-50">
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                FP32 TFLOPS
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">91.6</td>
            <td class="border border-gray-300 px-4 py-2 text-center">67</td>
        </tr>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                TF32 Tensor Core Flops*
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                183 | 366
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                378 | 756
            </td>
        </tr>
        <tr class="bg-gray-50">
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                FP16 Tensor Core Flops*
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                362 | 733
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                756 | 1513
            </td>
        </tr>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                FP8 Tensor Core TFLOPS
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">1466</td>
            <td class="border border-gray-300 px-4 py-2 text-center">3958</td>
        </tr>
        <tr class="bg-gray-50">
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Peak INT8 TOPS*
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                733 | 1466
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">3958</td>
        </tr>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Media Engine
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                0 NVENC, 5 NVDEC, 5 NVJPEG
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                0 NVENC, 7 NVDEC, 7 NVJPEG
            </td>
        </tr>
        <tr class="bg-gray-50">
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                L2 Cache
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">96 MB</td>
            <td class="border border-gray-300 px-4 py-2 text-center">50 MB</td>
        </tr>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Power
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                Up to 350 W
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                Up to 700 W
            </td>
        </tr>
        <tr class="bg-gray-50">
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Form Factor
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                Dual Slot Width
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                SXM5 - 8-way HGX
            </td>
        </tr>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Interconnect
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                PCIe 4.0 x16
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                PCIe 5.0 x16
            </td>
        </tr>
    </tbody>
</table>
<p class="text-sm text-gray-600 mt-2">*Without and with structured sparsity.</p>

Ultimately, the choice between the L40S and H100 depends on your specific needs.

### You may want to consider the L40S for:

-   **Cost-effective inference**, given that it offers roughly 40% of the inference performance of the H100 at ~30% of the cost.
-   **Small-model training and fine-tuning**, where the L40S provides sufficient performance without the need for the extensive capabilities of the H100.
-   **Versatile computing environments** striking a balance between AI, graphics, and general compute tasks without the need for MIG capabilities.
-   **Graphics-heavy applications** requiring advanced rendering and real-time processing.
-   **Prioritizing cost-efficiency and power efficiency**, along with the convenient integration of a dual-slot PCIe form factor.

### Whereas the H100 is better suited for:

-   **Training foundational models**, as it was specifically designed to handle the scale and complexity needed.
-   **Fine-tuning medium to large models** as a general rule, although the L40S can be a solid choice for mid-range needs depending on your specific requirements.
-   **Cutting-edge AI research**, large-scale simulations, and other deep learning applications that require maximum computational power.
-   **HPC environments** where speed, memory, bandwidth, and double-precision performance are critical.
-   **Intensive computational tasks** and workloads that don't involve high-end graphics and media rendering.

---

At **Vast.ai**, we know how difficult it can be to choose the right GPU – especially when the upfront cost to purchase this powerful hardware is so high. Our mission is to make advanced GPU technology more accessible by offering flexible, cost-effective rental options for everyone, everywhere. Our cloud GPU rental marketplace gives you fast access to a wide variety of machines at the lowest prices possible.

With **Vast**, you can save 5-6X on GPU compute. Explore our platform today to find the right GPU solution for your needs, without breaking the bank!


Choosing the right GPU can be challenging, whether for AI and graphics-intensive tasks, universal computing at data centers, or high-performance computing. This post compares two top NVIDIA GPUs, the L40S and H100, to help you find the one that best suits your needs.

NVIDIA H100 vs. L40s: Power Meets Versatility


When it comes to high-performance computing (HPC), the NVIDIA H100 is among the best of the best GPUs on the market. Its predecessor, the NVIDIA A100, is also a very impressive GPU in its own right. From enterprise to exascale, these GPUs are enabling groundbreaking advancements in everything from AI research to complex scientific simulations.

Today we're taking a look at how the H100 and A100 compare with each other. Depending on your use case, one or the other might be better suited for your needs and preferences. So let's dive in!

## Introduction to the NVIDIA H100 and A100

As mentioned, the H100 GPU is the successor to the A100. It was specifically optimized for machine learning and designed to [deliver](https://developer.nvidia.com/blog/nvidia-hopper-architecture-in-depth/) an "order-of-magnitude performance leap for large-scale AI and HPC" over the A100, with "substantial improvements in architectural efficiency."

Based on the innovative Hopper architecture, the H100 can speed up large language models (LLMs) by 30X over the A100, according to NVIDIA. It features fourth-gen Tensor Cores and – unlike the A100 – a dedicated Transformer Engine to solve trillion-parameter language models.

Whether it's used in smaller enterprise systems or massive, unified GPU clusters, the H100 delivers efficient scalability thanks to its combination of fourth-gen NVLink (with 900 GB/s of GPU-to-GPU interconnect), NDR Quantum-2 InfiniBand networking (speeding up communication by every GPU across nodes), and PCIe Gen5. With superior FP64 and FP8 performance, the H100 is ideal for next-gen AI workloads.

The A100 GPU is known for its versatility, however. Built on the Ampere architecture, it was groundbreaking when released in 2020, providing up to 20X higher performance over the prior NVIDIA Volta generation. It can efficiently scale up or be partitioned using Multi-Instance GPU (MIG) into seven isolated GPU instances, just like the H100, which is ideal for shifting workload demands.

In short, the A100 excels in both training and inference tasks, and while it may not offer the sheer power of the H100, its robust FP64 performance makes it a great fit for scientific simulations and data analytics.

## Key Differences: H100 vs. A100

The H100 has an updated chip design and various other factors that distinguish it from the A100. The following are a few that are important to consider:

### Memory Bandwidth

The A100 has a memory bandwidth of about 2 TB/s, which is plenty for most use cases; however, the H100 offers an impressive 3.35 TB/s, making it ideal for workloads where data transfer bottlenecks may occur.

### CUDA Cores and Tensor Cores

A higher number of CUDA and Tensor Cores means a GPU can perform more parallel computations simultaneously. With 14,592 CUDA Cores and 456 Tensor Cores, the H100 outperforms the A100’s 6,912 CUDA Cores and 432 Tensor Cores, delivering superior performance in applications that can leverage increased parallelism.

### Confidential Computing

The H100's Hopper architecture introduced NVIDIA's new confidential computing environment. It protects data-in-use by performing computation in a trusted execution environment (TEE) that is both hardware-based and attested. This means you essentially have absolute security for use cases where security, privacy, and regulatory compliance are paramount.

### Tensor Memory Accelerator (TMA)

Another feature of the H100's Hopper architecture is the TMA unit, which can transfer large blocks of data efficiently between global memory and shared memory. It supports asynchronous copies between thread blocks in a cluster as well, optimizing data movement and memory management for tensor operations. Ultimately, this enhances the efficiency and speed of large-scale AI and deep learning tasks.

### MIG Capability

The Multi-Instance GPU (MIG) feature is present in both the H100 and A100, providing flexible workload management so you can run multiple LLMs in parallel, for instance. The H100's MIG capability is more robust, however, with better resource allocation and more memory and compute power to split.

## Performance Benchmarks

According to NVIDIA's own benchmarks, the H100 offers up to 30X better inference performance than the A100. In another test where the A100 and H100 were used in clusters to train two large language models (LLMs), the H100 demonstrated remarkable speed improvements. The H100's new NVLink Switch System provided an even bigger boost, enabling the H100 cluster to train the models up to nine times faster than the A100 cluster:

![H100 vs A100 Speed Comparison](/uploads/Speed-up-over-A100.png)

## Choosing the Right GPU

So what does all this mean for you? The best GPU for your needs will depend on numerous factors. Here's a quick outline of the various features and specs of the NVIDIA A100 and H100:

<table class="w-full border-collapse border border-gray-300">
  <thead>
    <tr class="bg-gray-100">
      <th class="border border-gray-300 px-4 py-2 text-left font-semibold">Feature</th>
      <th class="border border-gray-300 px-4 py-2 text-center font-semibold">A100</th>
      <th class="border border-gray-300 px-4 py-2 text-center font-semibold">H100</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td class="border border-gray-300 px-4 py-2 font-semibold">GPU Architecture</td>
      <td class="border border-gray-300 px-4 py-2 text-center">Ampere</td>
      <td class="border border-gray-300 px-4 py-2 text-center">Hopper</td>
    </tr>
    <tr class="bg-gray-50">
      <td class="border border-gray-300 px-4 py-2 font-semibold">GPU Memory</td>
      <td class="border border-gray-300 px-4 py-2 text-center">40 or 80 GB HBM2e</td>
      <td class="border border-gray-300 px-4 py-2 text-center">80 MB HBM3</td>
    </tr>
    <tr>
      <td class="border border-gray-300 px-4 py-2 font-semibold">GPU Memory Bandwidth</td>
      <td class="border border-gray-300 px-4 py-2 text-center">1.6 to 2 TB/s</td>
      <td class="border border-gray-300 px-4 py-2 text-center">3.35 TB/s</td>
    </tr>
    <tr class="bg-gray-50">
      <td class="border border-gray-300 px-4 py-2 font-semibold">CUDA Cores</td>
      <td class="border border-gray-300 px-4 py-2 text-center">6912</td>
      <td class="border border-gray-300 px-4 py-2 text-center">14,592</td>
    </tr>
    <tr>
      <td class="border border-gray-300 px-4 py-2 font-semibold">FP64 TFLOPS</td>
      <td class="border border-gray-300 px-4 py-2 text-center">9.7</td>
      <td class="border border-gray-300 px-4 py-2 text-center">33.5</td>
    </tr>
    <tr class="bg-gray-50">
      <td class="border border-gray-300 px-4 py-2 font-semibold">FP32 TFLOPS</td>
      <td class="border border-gray-300 px-4 py-2 text-center">19.6</td>
      <td class="border border-gray-300 px-4 py-2 text-center">67</td>
    </tr>
    <tr>
      <td class="border border-gray-300 px-4 py-2 font-semibold">TF32 Tensor Core Flops*</td>
      <td class="border border-gray-300 px-4 py-2 text-center">156 | 312</td>
      <td class="border border-gray-300 px-4 py-2 text-center">378 | 756</td>
    </tr>
    <tr class="bg-gray-50">
      <td class="border border-gray-300 px-4 py-2 font-semibold">FP16 Tensor Core Flops*</td>
      <td class="border border-gray-300 px-4 py-2 text-center">312 | 624</td>
      <td class="border border-gray-300 px-4 py-2 text-center">756 | 1513</td>
    </tr>
    <tr>
      <td class="border border-gray-300 px-4 py-2 font-semibold">FP8 Tensor Core TFLOPS</td>
      <td class="border border-gray-300 px-4 py-2 text-center">N/A</td>
      <td class="border border-gray-300 px-4 py-2 text-center">3958</td>
    </tr>
    <tr class="bg-gray-50">
      <td class="border border-gray-300 px-4 py-2 font-semibold">Peak INT8 TOPS*</td>
      <td class="border border-gray-300 px-4 py-2 text-center">624 | 1248</td>
      <td class="border border-gray-300 px-4 py-2 text-center">3958</td>
    </tr>
    <tr>
      <td class="border border-gray-300 px-4 py-2 font-semibold">Media Engine</td>
      <td class="border border-gray-300 px-4 py-2 text-center">0 NVENC<br>5 NVDEC<br>5 NVJPEG</td>
      <td class="border border-gray-300 px-4 py-2 text-center">0 NVENC<br>7 NVDEC<br>7 NVJPEG</td>
    </tr>
    <tr class="bg-gray-50">
      <td class="border border-gray-300 px-4 py-2 font-semibold">L2 Cache</td>
      <td class="border border-gray-300 px-4 py-2 text-center">40 MB</td>
      <td class="border border-gray-300 px-4 py-2 text-center">50 MB</td>
    </tr>
    <tr>
      <td class="border border-gray-300 px-4 py-2 font-semibold">Power</td>
      <td class="border border-gray-300 px-4 py-2 text-center">Up to 400 W</td>
      <td class="border border-gray-300 px-4 py-2 text-center">Up to 700 W</td>
    </tr>
    <tr class="bg-gray-50">
      <td class="border border-gray-300 px-4 py-2 font-semibold">Form Factor</td>
      <td class="border border-gray-300 px-4 py-2 text-center">SXM4 - 8-way HGX</td>
      <td class="border border-gray-300 px-4 py-2 text-center">SXM5 - 8-way HGX</td>
    </tr>
    <tr>
      <td class="border border-gray-300 px-4 py-2 font-semibold">Interconnect</td>
      <td class="border border-gray-300 px-4 py-2 text-center">PCIe 4.0 x16</td>
      <td class="border border-gray-300 px-4 py-2 text-center">PCIe 5.0 x16</td>
    </tr>
  </tbody>
</table>

\*Without and with structured sparsity.

---

## Use Cases

When it comes to use cases, you may want to consider some general guidelines.

### The A100 is well suited for:

-   Versatile AI workloads including training and inference.
-   Applications needing a balance of performance and cost-efficiency.
-   Scenarios where ultra-high precision isn't the priority.

### The H100 is ideal for:

-   Cutting-edge AI training, large-scale simulations, and other HPC workloads.
-   Environments where computational speed and memory bandwidth are critical.
-   Future-proofing investments in AI infrastructure.

---

When deciding between the A100 and H100, consider your specific workload requirements. If you need top-tier double-precision performance and superior memory bandwidth, or you're dealing with next-gen HPC at datacenter scale and trillion-parameter AI, the H100 is the clear winner. For a more versatile and cost-effective solution that still delivers powerful AI performance, the A100 is a solid choice.

At Vast.ai, we understand the importance of finding the right GPU for your needs as well as your budget. Our cloud GPU rental marketplace offers a variety of machines at the lowest prices possible, saving you 5-6X on GPU compute. Whether you prefer on-demand pricing or interruptible instances through spot auction-based pricing, we've got you covered.

We invite you to discover what Vast can do for you and accelerate your projects with the right GPU today!


Explore the differences between NVIDIA's H100 and A100 GPUs, from architectural advancements to performance benchmarks.

H100 vs A100: Comparing Two Powerhouse GPUs


For both technology enthusiasts and industry professionals, keeping a close eye on the latest GPU advancements isn't just a hobby – it's crucial for staying competitive and maximizing performance and compute power. Here at Vast.ai, we're just as excited to track the biggest leaps forward in graphics technology.

Today, we're looking at two GPUs in particular: the **NVIDIA RTX 4090** and its formidable successor, the **RTX 5090**. The RTX 4090 has already earned its reputation for performance, while the RTX 5090 has recently made its debut and is setting new standards in the industry.

Let's explore how the two compare with each other\!

### Spotlight on NVIDIA GeForce RTX 4090

The **RTX 4090** made waves as the top-tier consumer-grade GPU of its generation. Built on the AD102 chip and Ada Lovelace architecture, it quickly became the go-to choice for high-end gaming and demanding professional workloads.

With a triple-slot design, the RTX 4090 boasts **128 third-gen RT cores** and **512 fourth-gen Tensor cores**, elevating ray-tracing performance and speeding up AI-driven tasks like **DLSS 3** in games.

Plus, it's packed with **16,384 CUDA cores** and has **24GB of GDDR6X memory** with a maximum bandwidth of **1,008 GB/s** and a **384-bit memory bus** interface. Error-correcting code (ECC) can be enabled, but this reduces performance while making no difference in GPU rendering – so keeping it turned off is appropriate for most uses when you're prioritizing speed.

Due to its high processing capabilities, the RTX 4090 draws plenty of power: **450 watts**. It requires an 850-watt power supply and robust cooling solutions, making it one of the more demanding GPUs to run, to this day.

When it launched in October 2022, the RTX 4090 raised the bar for consumer GPUs. It may no longer sit at the top, but its capabilities continue to make it a powerhouse for serious workloads and high-end gaming.

### The New NVIDIA GeForce RTX 5090

The RTX 5090, released earlier this year, is a game-changer, redefining high-end performance in the consumer space with its groundbreaking features and next-gen architecture. With the RTX 50 series, we've now moved beyond NVIDIA's Ada Lovelace and into the era of the Blackwell architecture.

Joining the new Blackwell GPU lineup, the flagship GeForce RTX 5090 utilizes the "physically monolithic" **GB202 die**. Among its highlights are a **512-bit memory bus** and **32GB of next-gen GDDR7 memory** (the successor to the GDDR6/X memory used by the RTX 40-series GPUs). The RTX 5090 also boasts memory bandwidth of 1,792 GB/s, a substantial jump from the RTX 4090's 1,008 GB/s​.

Here's a quick side-by-side comparison of the RTX 5090 and RTX 4090's specs:

<table class="w-full border-collapse border border-gray-300">
    <thead>
        <tr class="bg-gray-100">
            <th class="border border-gray-300 px-4 py-2 text-left font-semibold">
                Spec
            </th>
            <th class="border border-gray-300 px-4 py-2 text-center font-semibold">
                RTX 4090
            </th>
            <th class="border border-gray-300 px-4 py-2 text-center font-semibold">
                RTX 5090
            </th>
        </tr>
    </thead>
    <tbody>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Architecture
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">AD102</td>
            <td class="border border-gray-300 px-4 py-2 text-center">GB202</td>
        </tr>
        <tr class="bg-gray-50">
            <td class="border border-gray-300 px-4 py-2 font-semibold">VRAM</td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                24GB GDDR6X
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                32GB GDDR7
            </td>
        </tr>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                VRAM Speed
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">21Gbps</td>
            <td class="border border-gray-300 px-4 py-2 text-center">28Gbps</td>
        </tr>
        <tr class="bg-gray-50">
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                VRAM Bus Width
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">384</td>
            <td class="border border-gray-300 px-4 py-2 text-center">512</td>
        </tr>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Bandwidth
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                1.01TB/s
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                1792GB/s
            </td>
        </tr>
        <tr class="bg-gray-50">
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Streaming Multiprocessors (SMs)
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">128</td>
            <td class="border border-gray-300 px-4 py-2 text-center">170</td>
        </tr>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Tensor Cores
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">512</td>
            <td class="border border-gray-300 px-4 py-2 text-center">680</td>
        </tr>
        <tr class="bg-gray-50">
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Ray Tracing Cores
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">128</td>
            <td class="border border-gray-300 px-4 py-2 text-center">170</td>
        </tr>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Boost Clock
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                2520 MHz
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                2407 MHz
            </td>
        </tr>
        <tr class="bg-gray-50">
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Total Graphics Power (TGP)
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">450W</td>
            <td class="border border-gray-300 px-4 py-2 text-center">575W</td>
        </tr>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Recommended Power Supply
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">850W</td>
            <td class="border border-gray-300 px-4 py-2 text-center">1000W</td>
        </tr>
        <tr class="bg-gray-50">
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Launch Date
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                Sept. 20, 2022
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                Jan. 30, 2025
            </td>
        </tr>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Launch Price
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">$1,599</td>
            <td class="border border-gray-300 px-4 py-2 text-center">$1,999</td>
        </tr>
    </tbody>
</table>

Additionally, the RTX 5090 comes equipped with **170 streaming multiprocessors** (SMs) and **21,760 CUDA cores**, as well as **170 fourth-gen RT cores** and **680 fifth-gen Tensor cores** – up from 128 and 512 respectively in the RTX 4090\.

In terms of power consumption, the RTX 5090 has a total graphics power (TGP) of **575 watts**, considerably higher than the RTX 4090's 450 watts.

When it comes to demanding AI and video workloads, the RTX 5090 shows clear gains over its predecessor. In Procyon's AI XL (FP16) benchmark, it outperformed the RTX 4090 by 40%, and in PugetBench's DaVinci Resolve test, it delivered a 12% performance boost.

Next-generation gaming features are a major draw for the RTX 5090\. One of the most significant leaps forward comes from **DLSS 4**, NVIDIA's AI-powered upscaling technology that further enhances performance and image quality. With advancements like **Multi Frame Generation**, **Neural Rendering**, and **Mega Geometry** – and with 4K 240FPS gaming becoming more attainable – the RTX 5090 is perfectly positioned to tackle the most demanding gaming scenarios and deliver the best possible gameplay experience.

(For a detailed examination of the RTX 5090, delving into its gaming features, real-world performance, and more, see our in-depth post [here](https://vast.ai/article/nvidia-geforce-rtx-5090-specs-everything-you-need-to-know).)

### Concluding Thoughts

Even after the release of the RTX 5090, it's important to recognize that the RTX 4090 still delivers exceptional performance, handling everything available at 4K with ease. The RTX 5090, while impressive, may be considered more of a luxury upgrade than a necessity for gamers and professionals who can (or already do) benefit from the power of the RTX 4090\. It really depends on whether the extra power will make enough of a difference for your needs.

Cost is another critical factor. The RTX 5090 is a top-of-the-line machine with a matching price tag. And the RTX 4090 is no slouch, either, still commanding a steep price to this day, over two years after its release.

Here at Vast.ai, we take pride in providing access to powerful machines for rent on the cloud at unbeatable prices. Vast offers unmatched flexibility, enabling you to utilize state-of-the-art technology without the expense and upkeep of purchasing your own hardware.

Whether you're looking to explore the capabilities of high-end consumer GPUs like the RTX 4090 or 5090, or check out any other machine on Vast, we'd be happy to help you discover the possibilities\!


A detailed comparison between NVIDIA's RTX 4090 and RTX 5090 GPUs, analyzing their performance, features, and value for both gaming and professional workloads.

Posts about: Hardware Reviews

NVIDIA RTX 4090 vs. A100: Two Powerhouses, Two Purposes

NVIDIA H100 vs. L40s: Power Meets Versatility

H100 vs A100: Comparing Two Powerhouse GPUs

NVIDIA RTX 4090 vs. RTX 5090: How They Stack Up