Everything You Need to Know About the Nvidia RTX 5090 GPU

The Nvidia GeForce RTX 5090 is the latest flagship graphics card in Nvidia’s GeForce lineup, and it’s making waves for good reason. Announced at CES 2025 and launched on January 30, 2025 at an MSRP of $1,999, the RTX 5090 represents a significant leap in GPU performance and capabilities. This approachable yet powerful GPU is built for gamers, creators, and AI developers alike – anyone who needs extreme graphics and compute horsepower. In this article, we’ll break down everything you need to know about the RTX 5090, from its official specs and groundbreaking features to how it compares with previous GPUs (like the RTX 4090, A100, and H100) and why it’s a game-changer for cloud GPU computing. By the end, you’ll understand the RTX 5090’s strengths and how you can harness its power on Runpod’s cloud platform. Let’s dive in! 🚀

Mid-2025 Updates (Latest News & Availability)

Mid-2025 Update: Since this article’s initial publication, several key developments have emerged:

Driver Improvements: Nvidia has released multiple driver updates (through Q2 2025) that improve RTX 5090 stability and performance. The April 2025 Game Ready driver (v576.02) resolved many early issues (crashes, black screens) and even boosted synthetic benchmark scores by up to ~8% on RTX 50-series GPUs . These updates mean a smoother, more reliable experience for RTX 5090 users.
Global Cloud Availability: Runpod has expanded RTX 5090 availability across its global data centers, including multiple U.S. regions . This widespread rollout makes it easier for AI/ML developers worldwide to access RTX 5090 power on-demand, effectively providing a Nvidia GPU cloud in the U.S. and beyond for low-latency machine learning compute.
Pricing Trends: The RTX 5090 remains a high-demand product. Its $1,999 USD MSRP hasn’t budged, and in mid-2025 it’s still hard to find a 5090 under ~$3,000 in retail due to limited supply . On Runpod’s cloud, however, you can rent RTX 5090 instances starting around $0.94/hr (even lower with community-tier instances), offering affordable high-performance compute for AI compared to spending thousands up front.

Nvidia RTX 5090 Overview (Blackwell Architecture & Launch)

The GeForce RTX 5090 is Nvidia’s newest “enthusiast-class” GPU, sitting at the top of the GeForce RTX 50 series (codenamed Blackwell architecture). It’s officially the fastest GeForce GPU to date, featuring a massive 92 billion transistors and delivering over 3,352 trillion AI operations per second (TOPS) of compute performance . In practical terms, Nvidia claims the RTX 5090 outperforms the previous-generation RTX 4090 by up to 2× in performance , thanks to architectural improvements and next-gen technologies like DLSS 4.

Blackwell Architecture: The RTX 5090 is built on Nvidia’s new Blackwell architecture, which succeeds the Ada Lovelace generation. Blackwell introduces advanced AI-driven rendering features (such as Neural Shaders and DLSS 4) and improved ray tracing capabilities. It merges the best of Nvidia’s gaming and data center GPU tech into one architecture. Jensen Huang (Nvidia’s CEO) described Blackwell as “the most significant computer graphics innovation since programmable shading” , underscoring how important this launch is for the industry.

Release and Availability: The RTX 5090 (along with its sibling RTX 5080) was unveiled in early January 2025 and hit the market on Jan 30, 2025. Its launch price is $1,999 for the Nvidia Founders Edition . As an ultra high-end card, it’s in limited supply (and many expect third-party overclocked models to cost even more). For those who don’t want to spend two grand upfront, leveraging cloud GPUs is an attractive alternative – for example, you can access RTX 5090-class performance on-demand via a platform like Runpod’s GPU Cloud, without the hefty upfront cost. (More on this later!)

Key Specifications and Features of the RTX 5090

What makes the RTX 5090 so powerful? Here’s a rundown of its official specs and standout features:

CUDA Cores: 21,760 CUDA cores (spread across 170 Streaming Multiprocessors) provide massive parallel processing power for graphics and compute . This is a big jump from the 16,384 cores in the RTX 4090, enabling greater throughput in shaders and general GPU tasks.
Clock Speeds: ~2.0 GHz base clock and 2.41 GHz boost clock out of the box. Despite the huge core count, the RTX 5090 still achieves high frequencies, translating to blazing fast frame rates and compute times.
Memory (VRAM): 32 GB of ultra-fast GDDR7 memory on a 512-bit bus. This enormous VRAM size is 33% more than the 24 GB on the 4090. The new GDDR7 memory technology delivers up to 1,792 GB/s of memory bandwidth , ensuring the GPU cores stay fed with data. Large memory capacity is great for high-resolution textures, big datasets, or training larger AI models.
Tensor Cores (AI Acceleration): 5th-generation Tensor Cores provide dedicated AI horsepower. The RTX 5090 has 680 Tensor Cores (these power AI features like DLSS, AI upscaling, and tensor computations for machine learning). It achieves 3352 AI TOPS (trillions of operations per second in AI workloads) , making it exceptionally capable for deep learning tasks.
RT Cores (Ray Tracing): 4th-generation RT Cores enable advanced ray tracing effects with lower performance hits. There are 170 RT Cores in the 5090, delivering cutting-edge real-time ray tracing and global illumination in games and rendering apps. This generation improves ray-triangle intersection performance and introduces new capabilities for rendering realistic lighting and shadows.
New DLSS 4: The 50-series introduces DLSS 4, Nvidia’s latest AI-driven upscaling and frame generation suite. Multi Frame Generation can use AI to interpolate additional frames, boosting frame rates dramatically (up to 3 AI-generated frames for each rendered frame) . Combined with DLSS Super Resolution and Ray Reconstruction, DLSS 4 can improve performance by up to 8× in supported games while maintaining image quality . This means even the most demanding games can run at high FPS on the RTX 5090, especially at 4K and beyond.
Neural Rendering & Shaders: Blackwell GPUs like the 5090 introduce RTX Neural Shaders, which use small AI networks in the rendering pipeline to achieve effects that were previously impossible in real-time . For example, RTX Neural Faces use AI to render highly realistic human faces in real time . These features highlight how the RTX 5090 isn’t just about raw power, but also smarter rendering through AI.
Connectivity: Supports the latest standards – PCIe 5.0 for maximum host bandwidth, DisplayPort 2.1 and HDMI 2.1 outputs (ready for ultra-high-refresh 4K/8K displays). It can drive up to 4 monitors, and supports 8K resolution up to 165 Hz (with DSC) or 4K at 480 Hz, which is ideal for next-gen displays .
Power and Cooling: This card is a beast in power consumption, rated at 575 W TDP for the Founders Edition . It uses a new dual-slot “Revolutionary Double Flow-Through” cooler design that Nvidia engineered to handle the heat within a 2-slot form factor. The cooler features a 3D vapor chamber, integrated heatpipes, and dual-sided fans for double the airflow of a traditional card . (Yes, the RTX 5090 is both extremely fast and runs hot!) Due to its power draw, it uses the 16-pin PCIe 5.0 power connector and requires a strong PSU if installed in a PC.

As you can see, the RTX 5090’s specs firmly establish it as a generational leap over its predecessors. It packs more cores, higher clocks, more memory, and new tech features that push the boundaries of what a single GPU can do. For gamers, this means top-of-the-line performance with all eye-candy enabled. For creators and AI practitioners, it means less waiting on renders or model training – and the ability to work with bigger scenes and datasets in GPU memory.

(Fun fact: With 21,760 CUDA cores, the RTX 5090 has 33% more cores than the RTX 4090, and even more transistors than Nvidia’s current data-center GPUs. It’s truly an “AI supercomputer” on a card.)

Performance Improvements: RTX 5090 vs RTX 4090 (and Previous GPUs)

How much faster is the RTX 5090 in the real world? Early benchmarks and Nvidia’s own figures show that the RTX 5090 handily outpaces the RTX 4090, often by a sizeable margin. Thanks to the core count increase and architectural upgrades, raw throughput is higher. For example, in traditional rasterized graphics, the RTX 5090’s shader performance can be ~25–40% higher than the 4090 at 4K resolution in many games. In ray-traced workloads or hybrid rendering (where DLSS 4’s Multi Frame Generation is leveraged), the performance uplift can approach Nvidia’s “up to 2× faster” claim in supported titles .

A few notable comparisons to frame the generational strengths:

RTX 5090 vs RTX 4090: The 5090 has more memory (32 GB vs 24 GB), which is beneficial for handling 8K gaming or large texture packs. Its higher CUDA core count and slightly higher clocks give it a strong lead in compute-heavy tasks. In GPU compute benchmarks (like Blender rendering or AI model inference), it can complete tasks significantly quicker – great news for creators. In gaming, especially with ray tracing, the 4th-gen RT Cores and DLSS 4 help the 5090 maintain much higher frame rates than the 4090 in the latest titles with max settings. In short, if the RTX 4090 could do 4K@120fps in a game, the 5090 might push that to ~4K@144fps or higher in the same game, or allow enabling even more advanced settings. It’s the new king of performance among single GPUs.
RTX 5090 vs RTX 3090 (two generations back): To highlight the progress, the 5090 offers roughly 4× the performance of an RTX 3090 in many scenarios, thanks to two generations of improvements. It also has double the VRAM of the 3090 (which had 16 GB or 24 GB depending on model) and far superior ray tracing. If you’re still on a 30-series, the jump to a 50-series is enormous.
Power Efficiency: One thing to note is that performance-per-watt hasn’t improved dramatically – the RTX 5090 draws more power to achieve its gains. Its 575W consumption is substantially higher than the 4090’s ~450W. That said, when you consider performance per frame or per training iteration, the 5090 is doing more work for the power. Nvidia’s focus this generation was brute-force performance and AI features, so expect to trade higher electricity and heat for the speed. In a cloud environment like Runpod’s Secure Cloud, this isn’t your problem – the data center handles cooling and power delivery, so you get the benefits of speed without worrying about utility bills or heat in your office! 🎉

It’s also worth mentioning that Nvidia’s closest competition (AMD’s GPUs or other accelerators) currently has nothing that matches the RTX 5090 in sheer performance. This card stands in a league of its own for now. For users, that means if you need the best single-GPU performance available in 2025, the 5090 is it.

RTX 5090 for AI, Machine Learning, and Professional Workloads

Beyond gaming, the RTX 5090 is extremely attractive for AI researchers, ML engineers, and content creators. Nvidia has effectively blurred the line between consumer GPUs and professional AI accelerators with this generation. Here are some reasons the RTX 5090 shines for non-gaming tasks:

Massive AI Compute: The combination of 21k+ CUDA cores and 5th-gen Tensor Cores means the 5090 can crank through neural network operations at incredible speed. It supports newer precision formats like FP8 and even experimental FP4 (a first for consumer GPUs) , which can double AI throughput for certain models (with minimal impact on accuracy when algorithms are tuned for it). In practice, this means faster training times and higher inference throughput. For example, if you fine-tune a transformer model or run Stable Diffusion image generations, the RTX 5090 will complete the task much faster than previous-gen cards.
Sufficient VRAM for Many Models: 32 GB VRAM is a boon for AI workloads. It allows training larger models or batches locally on the GPU without running out of memory. Many popular models (like GPT-style language models up to ~13B parameters, or large image models) can fit in 32 GB in half-precision, enabling experimentation that previously required expensive 40GB+ cards (or multi-GPU setups). And for those truly massive models that do require more memory, you can always distribute across multiple GPUs in the cloud. The bottom line: the 5090’s memory capacity makes it much more practical for serious deep learning work than a 4090 (which sometimes was constrained by 24 GB).
Content Creation & Rendering: If you’re a 3D artist or video editor, the RTX 5090 is a dream. It has multiple NVENC hardware encoders (3× 9th-gen encoders) and 2× decoders, including AV1 encode/decode support . This means you can capture or stream at high resolutions with minimal performance impact, and render videos faster. Creative apps like Adobe Premiere Pro, DaVinci Resolve, Blender, etc., all take advantage of the extra CUDA cores and encoders – so tasks like 8K video editing, 3D rendering, or animation will be smoother and quicker.
Compared to A100/H100 (Data Center GPUs): Nvidia’s previous data center champions, the A100 (Ampere, 2020) and H100 (Hopper, 2022), were the go-to for AI labs due to their high VRAM (40–80 GB) and multi-precision tensor cores. Surprisingly, the RTX 5090 now offers more raw FP32 compute than those chips in many cases, thanks to its sheer core count and clock speed. It even exceeds the H100 in transistor count (92B vs ~80B) and matches many of its AI capabilities (like FP8 support). However, keep in mind the RTX 5090 is still a GeForce card – it lacks some pro features like ECC memory, NVLink peer-to-peer GPU interconnect, and hardware for multi-instance GPU (MIG) partitioning. For many AI practitioners, though, the 5090 provides data-center-like performance at a fraction of the cost. On Runpod’s cloud, you could spin up multiple RTX 5090 instances to tackle large training jobs, achieving results comparable to what was previously reserved for supercomputer-grade hardware. That’s a huge win for startups, researchers, and hobbyists who need affordable access to compute.

In summary, the RTX 5090 isn’t just about gaming – it’s a multi-purpose computational powerhouse. Nvidia has marketed it to gamers, creators, and developers, and we can see why: its capabilities in AI and professional workloads are as impressive as its gaming chops. If you’re in machine learning or any GPU-accelerated field, it’s worth paying attention to what the RTX 5090 brings to the table. And if the price or hardware setup is a barrier, remember you can always tap into this power through cloud services.

Harnessing the RTX 5090 on the Cloud (Advantages for Runpod Users)

If you’re excited about the RTX 5090’s capabilities, the next question is: how can you get access to one? While enthusiasts might install a 5090 in a desktop PC, many others will prefer a more flexible approach – like using cloud GPUs. This is where Runpod comes in. Runpod is an AI-focused cloud platform that offers GPU instances on-demand, including the latest Nvidia hardware. Here’s why using the RTX 5090 through Runpod’s cloud can be a smart, actionable choice:

No Upfront Hardware Cost: Instead of spending $2000 (or more) on a GPU, you can rent an RTX 5090 by the hour on Runpod’s platform. This pay-as-you-go model is cost-effective, especially if you only need extreme performance for short-term projects or occasional bursts. Check out our pricing page to see rates for RTX 5090 GPUs. You’ll likely find that running a 5090 in the cloud for a few hours or days is far cheaper than purchasing and maintaining one yourself.
Instant Scalability: With Runpod, you’re not limited to one GPU. Need to train a model across multiple GPUs? You could spin up two, four, or even more RTX 5090 instances in parallel to slash training time. Cloud GPUs let you scale your infrastructure with a few clicks or API calls. And when the job’s done, you can shut them down to stop billing. This kind of flexibility is nearly impossible with on-prem hardware, but it’s a core benefit of the cloud.
Secure Cloud Environment: Worried about reliability or security? Runpod’s Secure Cloud runs on Tier 3/4 datacenter infrastructure with trusted partners . That means enterprise-grade reliability, redundant power and networking, and strict security protocols. Your data and workloads run in an isolated, safe environment. This is perfect for sensitive AI projects or enterprise use cases where you need to ensure everything stays up and protected. (Runpod also offers a Community Cloud option for cost savings, but for top-notch stability with hardware like the 5090, Secure Cloud is recommended.)
Bring Your Own Container: Already have a specific environment or set of libraries you need? No problem – Runpod lets you deploy any containerized workload easily. You can bring your own Docker container with your preferred framework, drivers, and dependencies, and run it on an RTX 5090 instance in the cloud. This means no fiddling with setup each time; just push your container and go. (See our guide on building your own ML Docker container for tips on customizing your environment.) The ability to use custom containers ensures that migrating your workflow to the cloud is frictionless and mirrors your local setup.
Serverless Endpoints for Inference: If you’re deploying a machine learning model (say, an API or service that uses an AI model), you can take advantage of Runpod’s Serverless GPU Endpoints. This feature allows you to serve models on GPUs like the 5090 without managing a full server 24/7. You get autoscaling, 99.99% uptime, and you only pay when your endpoint is processing requests. It’s an actionable way to productionize the power of RTX 5090 for your application – let Runpod handle the infrastructure while you focus on your model and code.
Ease of Use and Quick Start: Runpod’s platform is designed to be developer-friendly. From an intuitive web UI to a CLI tool, you can launch a GPU instance in minutes. For example, you can log in, select an RTX 5090 from the available GPU types (or join a waitlist if it’s extremely new), choose a region, and hit deploy. Within moments, you’ll have a cloud machine ready – possibly pre-loaded with common frameworks like PyTorch or TensorFlow – and you can SSH or use Jupyter notebooks to get to work. It’s that simple. If you run into any issues or need guidance, the Runpod community and support are there to help (and our documentation covers all the details).

Cloud GPU Advantage: Using the RTX 5090 on Runpod gives you all the upside with none of the headaches. You don’t need to worry about supplying 575 watts of power, upgrading your PC case and cooling, or the card’s massive size – the cloud abstracts all of that. You can run workloads from any device (even a laptop or a smaller VM) and tap into 5090 performance remotely. This democratizes access to extreme GPU power; whether you’re a student, an indie developer, or a business, you can leverage the best GPU on the market on-demand. Plus, Runpod’s global data center locations ensure you can choose a region close to you for lower latency.

Finally, let’s not forget the actionable benefit: If you have an idea or project that could benefit from the RTX 5090, you can try it right now. For instance, want to see how fast your model trains with double the performance? Spin up a cloud instance on Runpod and find out today. You’re just a sign-up away from test-driving this GPU.

👉 Ready to experiment with the RTX 5090 in the cloud? Sign up for Runpod and launch an RTX 5090 instance to experience its power firsthand. Our platform makes it easy to get started, and you’ll be up and running in no time.

Frequently Asked Questions (FAQs)

Below we answer some common questions about the Nvidia RTX 5090:

When was the Nvidia RTX 5090 released?

A: Nvidia officially released the GeForce RTX 5090 on January 30, 2025. It was announced earlier that month during CES 2025 and hit store shelves (in very limited quantities) at the end of January .

What is the price of the RTX 5090?

A: The MSRP for the RTX 5090 is $1,999 USD for the Founders Edition at launch . This makes it one of the most expensive consumer GPUs ever released (for comparison, the RTX 4090 launched at $1,599 MSRP). Keep in mind that partner cards (from ASUS, MSI, etc.) or overclocked models may cost even more. Also, real-world prices can be higher if supply is constrained. If that price is out of reach, remember you can rent RTX 5090 performance on services like Runpod’s cloud much more affordably.

What are the key specs of the Nvidia RTX 5090?

A: The RTX 5090’s key specifications include: 21,760 CUDA cores, 170 RT Cores (4th gen), 680 Tensor Cores (5th gen), a base clock ~2.0 GHz (boost up to ~2.41 GHz), and 32 GB of GDDR7 VRAM on a 512-bit bus. The memory bandwidth is an impressive ~1.8 TB/s. It supports PCIe 5.0, DLSS 4, and many new features introduced with the Blackwell architecture. The card’s power draw is 575 W (so it’s power-hungry) , and it uses a dual-slot cooler with a new flow-through design. In short, it’s a spec monster, exceeding the RTX 4090 in almost every metric.

How much faster is the RTX 5090 compared to the RTX 4090?

A: Nvidia advertises up to 2× the performance of RTX 4090 in certain scenarios . In practical use, the speedup varies by workload. Many games and benchmarks show 20–50% higher framerates on the 5090 vs. the 4090 at 4K resolution. When advanced features like DLSS 4’s Multi Frame Generation are used, the gap can widen (since the 5090 can leverage those features fully to outrun the 4090). For GPU compute tasks (rendering, etc.), expect a solid boost (e.g., a render that took 60 seconds on 4090 might finish in ~40 seconds on 5090). The exact improvement depends on whether the task is GPU-bound and can use the extra cores and new features. Overall, the RTX 5090 cements itself as the new performance champion, leaving the 4090 in second place by a comfortable margin.

Is the RTX 5090 good for AI and machine learning?

A: Yes, absolutely. The RTX 5090 is one of the best GPUs you can get for AI/ML tasks right now. It has the latest-generation Tensor Cores that accelerate deep learning operations (matrices, convolutions, etc.), support for FP8/FP16 precision, and huge compute throughput. Training neural networks or running inference on the 5090 will generally be much faster than on previous-gen GPUs. Additionally, the 32 GB VRAM allows for training larger models or using bigger batch sizes than a 24 GB card like the 4090. Many researchers are excited because the 5090 brings near data-center GPU performance to a (relatively) accessible form factor. Whether you’re fine-tuning transformer models, doing computer vision, or running generative AI models (images, video, etc.), the RTX 5090 can handle it. And if your model is extremely large (needing more than 32GB), you can use techniques like model parallelism or simply run on multiple GPUs (which is easy to do on cloud platforms like Runpod).

How can I use or try the RTX 5090 through Runpod’s cloud?

A: Getting access to an RTX 5090 on Runpod is straightforward. First, create an account on Runpod (it’s free to sign up). Once logged in, you can deploy a new cloud GPU instance. In the instance configuration, you’ll see options for various GPU types – as the RTX 5090 becomes available on the platform, it will be listed (likely under a section for latest-generation GPUs or similar). Select the RTX 5090, choose your region and any other specs (like the amount of RAM, disk, etc., though the GPU comes fixed with its 32GB VRAM), and launch the instance. Within a couple of minutes, your cloud machine will be up and running with an RTX 5090 attached. You can then connect via SSH or our web interface, install any software or frameworks you need (if you didn’t use a pre-built image), and start using the GPU just like you would on a local machine. Runpod takes care of all the infrastructure. If the 5090 is in high demand or not immediately in stock, you might join a waitlist or be notified when you can get one – but we’re working hard to ensure availability to all our users. Once your instance is running, you’ll pay the hourly rate for usage, which you can find on our Pricing page (expect it to be competitive given the card’s capabilities). When you’re done, simply shut down the instance to stop billing. It’s a fantastic way to experiment with the RTX 5090 without any commitment. Pro tip: You can also use Runpod’s serverless endpoints to deploy models on the 5090 and only pay per request, which is great for scalable AI applications.

Nvidia’s RTX 5090 is a groundbreaking GPU that pushes the envelope in graphics and AI performance. Whether you’re eyeing it for ultra-high-end gaming or for accelerating compute-heavy projects, it delivers unprecedented power. And thanks to cloud services like Runpod, you don’t have to be a billionaire or build a fancy rig to leverage this power – it’s available to anyone with an internet connection and a credit card. We hope this guide has answered your questions and shown you the possibilities the RTX 5090 unlocks.

Ready to get started? The best way to truly appreciate the RTX 5090 is to use one. Spin up a cloud GPU on Runpod and see how the RTX 5090 can supercharge your work or projects. With our approachable platform and the RTX 5090’s raw performance, you’ll be able to innovate faster and achieve more – without the usual hardware hassles. Happy computing! 🚀

For more insights and updates on GPUs and cloud AI, be sure to check out the Runpod Blog and follow our latest posts. We regularly cover new hardware, best practices for ML in the cloud, and tips to get the most out of services like ours. Stay tuned, and let’s build something amazing with all this new power!

Sources

NVIDIA Newsroom – “NVIDIA Blackwell GeForce RTX 50 Series Opens New World of AI Computer Graphics”
PCGamesN – “Nvidia GeForce RTX 5090 release date, price, and specs”