
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


# NVIDIA GeForce RTX 5090: Release Announced

NVIDIA has officially unveiled the **GeForce RTX 5090**, the latest in its groundbreaking lineup of GPUs. The release date for this highly anticipated graphics card has been set for **January 30, 2025**, with availability expected alongside the RTX 5080, while the RTX 5070 Ti and RTX 5070 will follow in February.

During his CES keynote yesterday, NVIDIA CEO Jensen Huang introduced the RTX 5090 with a bold claim: the GPU is **two times faster than the RTX 4090,** thanks to DLSS 4 and the Blackwell architecture. But that's just the beginning.

## Official RTX 5090 Specs

Here are a few of the official specifications for the long-awaited **RTX 5090** (see more at the bottom of this page!):

-   **Architecture:** Built on the advanced Blackwell architecture.
-   **Memory:** 32GB of GDDR7 memory with an impressive memory bandwidth of 1,792GB/sec.
-   **CUDA Cores:** 21,760 cores, designed for maximum computational power.
-   **Power Requirements:** 575W total graphics power (TGP) with a recommended 1000W PSU.
-   **Display Support:** PCIe Gen 5 with DisplayPort 2.1b connectors supporting up to 8K at 165Hz.
-   **Gaming Chip:** Featuring the cutting-edge GB202 gaming chip.

In addition to a massive performance boost, the RTX 5090 features a redesigned **Founders Edition** with two double flow-through fans and a 3D vapor chamber, making it sleek enough to fit into small form factor PCs – a surprising change from the bulky RTX 4090.

These specs establish the RTX 5090 as the ultimate tool for gamers, creators, and professionals who demand peak performance.

## Beyond the RTX 5090: The RTX 50-Series Lineup

The younger siblings in the 50-series lineup – the RTX 5080, RTX 5070 Ti, and RTX 5070 – will also deliver outstanding capabilities:

-   **RTX 5080:** 16GB of GDDR7 memory, 10,752 CUDA cores, and a memory bandwidth of 960GB/sec. NVIDIA claims this card is twice as fast as the RTX 4080, with a total power consumption of 360 watts and recommended 850W PSU.
-   **RTX 5070 Ti:** 16GB of GDDR7, 8,960 CUDA cores, and a memory bandwidth of 896GB/sec, plus 300W TGP and 750W PSU.
-   **RTX 5070:** 12GB of GDDR7, 6,144 CUDA cores, and a bandwidth of 672 GB/sec, plus 250W TGP with just 650W PSU. It boasts "RTX 4090 performance at $549," according to Huang.

All three serve as excellent choices for those seeking high performance without committing to the premium price of the flagship model. – which brings us to the cost of these GPUs...

## RTX 50-Series Pricing and Availability

The RTX 5090 will debut at **$1,999** and will hit shelves on January 30, 2025, alongside the RTX 5080 priced at $999. The RTX 5070 Ti and RTX 5070, priced at **$749** and **$549** respectively, will launch in February.

With this lineup, NVIDIA is offering a range of options to meet different performance needs and budgets. Let's take a look at what else sets the RTX 50-series apart from its predecessors.

## DLSS 4 and Neural Tech: What's New?

During his keynote speech, Huang showcased the RTX Blackwell GPUs with an impressive real-time rendering demo that highlighted the latest advancements. The demonstration featured RTX Neural Materials, RTX Neural Faces, text-to-animation capabilities, and **DLSS 4** in action.

As Huang put it, "The new generation of DLSS can generate beyond frames – it can predict the future. We used GeForce to enable AI, and now AI is revolutionizing GeForce."

**Multi Frame Generation** is a highlight of DLSS 4, using AI to extrapolate and insert up to three additional frames between traditionally rendered frames, boosting frame rates by up to 8x.

A standout feature, **RTX Neural Shaders**, compresses in-game textures more efficiently and delivers film-quality shading and lighting in real time. Another feature, called **RTX Neural Faces**, uses generative AI to create lifelike skin, hair, and facial details.

Not only that, but NVIDIA is also bringing the RTX 50-series to laptops starting in March 2025. The laptop version of the RTX 5090 will feature 24GB of GDDR7 memory, making it a powerhouse for users on the go.

## Experience Cutting-Edge Performance Now with Vast.ai

While the RTX 5090 is set to make a big splash in the GPU market, **you can already access top-tier computing power right now with Vast.ai.** Our platform offers flexible GPU rentals, enabling you to tackle your most demanding projects without breaking the bank or waiting for new hardware.

Explore our options and elevate your workflow with Vast. Don’t let hardware limitations hold you back – get started in just minutes, today!

### Additional Specifications:

| Spec                                    | RTX 5090            | RTX 5080            | RTX 5070 Ti        | RTX 5070        
| ----------------------------------------| ------------------- | ------------------- | ------------------ | ------------------ |
| **Architecture**                        | GB202               | GB203               | GB203              | GB205              |
| **VRAM**                                | 32GB                | 16GB                | 16GB               | 12GB               |
| **VRAM Speed**                          | 28Gbps              | 30Gbps              | 28Gbps             | 28Gbps             |
| **VRAM Bus Width**                      | 512                 | 256                 | 256                | 192                |
| **Bandwidth**                           | 1792 GB/s           | 960 GB/s            | 896 GB/s           | 672 GB/s           |
| **Streaming Multiprocessors (SMs)**     | 170                 | 84                  | 70                 | 48                 |
| **Tensor Cores**                        | 680                 | 336                 | 280                | 192                |
| **Ray Tracing Cores**                   | 170                 | 84                  | 70                 | 48                 |
| **Boost Clock**                         | 2407 Mhz            | 2617 Mhz            | 2452 Mhz           | 2512 Mhz           |
| **Total Graphics Power (TGP)**          | 575W                | 360W                | 300W               | 250W               |
| **Recommended Power Supply**            | 1000W               | 850W                | 750W               | 650W               |
| **Launch Date**                         | Jan. 30, 2025       | Jan. 30, 2025       | Feb. 2025          | Feb. 2025          |
| **Launch Price**                        | $1,999              | $999                | $749               | $549               |

NVIDIA has officially unveiled the GeForce RTX 5090, the latest in its groundbreaking lineup of GPUs.

NVIDIA GeForce RTX 5090: Release Announced


# Could the End Be Near for the NVIDIA GeForce RTX 4090?

Rumors are circulating that the high-end consumer GPU is on the verge of being discontinued. With production potentially stopping, the 4090 could soon be disappearing off the shelves to make way for NVIDIA's new flagship GPU: the [RTX 5090](https://vast.ai/article/nvidia-rtx-5090-out-by-christmas-rumors).

As with any unconfirmed reports, it's best to take this news with a grain of salt – but the possibility has already sparked plenty of buzz among GPU enthusiasts.

## What's Driving the Rumors?

Reports about the potential discontinuation of the RTX 4090 appear to have [originated](https://wccftech.com/nvidia-geforce-rtx-4090-4090d-discontinued-next-month-in-preparation-for-next-gen-rtx-5090-5090d-gpus/) on China’s Board Channels, a frequent source of supply chain rumors. According to these sources, NVIDIA may stop producing the RTX 4090, along with its China-specific RTX 4090D variant, as early as October, with inventory expected to dwindle through the rest of the year.

But given that it's already late October without any official announcements from NVIDIA, the RTX 4090's availability seems secure in the near term. Still, we can't rule out any sudden shifts as NVIDIA prepares for the release of the next-gen RTX 5000 series.

According to rumors, the launch date of the new [flagship Blackwell RTX 5090 GPU](https://vast.ai/article/nvidia-rtx-5090-out-by-christmas-rumors) will be either in late 2024 or in early January at CES 2025. That timing does line up with NVIDIA potentially pulling the plug on the RTX 4090 sooner rather than later. Plus, the move would allow add-in board (AIB) partners to clear their stock of the RTX 4090 ahead of the RTX 5000-series release.

## Potential Impact on Pricing and Availability

Ending production of the RTX 4090 now (or very soon) would give NVIDIA's partners the holiday season to move existing inventory. This could result in shortages during the lead-up to Christmas as supplies run low, causing prices to spike as stock dwindles.

That said, with the highly anticipated RTX 5090 on the horizon, many buyers may hold off and wait for the new flagship release before making any purchase decisions. There's a chance this could put downward pressure on RTX 4090 prices, making it a more attractive option in the meantime.

It's hard to predict how things will unfold. Without official confirmation from NVIDIA, everything is merely speculation – and for now, it's a waiting game.

## Exploring Your GPU Options

While the future of the RTX 4090 remains uncertain, and the next-gen RTX 5090 will likely come with a steep price tag, one thing is clear: our cloud GPU rental platform allows you to access powerful GPUs at the most competitive rates available.

You don't need to make any huge purchases or commit to a single machine. With Vast.ai, you have the flexibility to choose from a range of GPUs that best fit your project, budget, and performance requirements at any given moment. Whether you're fine-tuning deep learning models, running complex simulations, or pushing your gaming rig to new heights, we're here to connect you with the GPU compute power that meets your exact needs.

[Discover the possibilities](https://vast.ai/) with Vast.ai today.


Rumors are circulating that the high-end consumer GPU is on the verge of being discontinued.

NVIDIA RTX 4090 to Be Discontinued Sooner Rather Than Later?


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: Rtx Series

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

NVIDIA GeForce RTX 5090: Release Announced

NVIDIA RTX 4090 to Be Discontinued Sooner Rather Than Later?

H100 vs A100: Comparing Two Powerhouse GPUs

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