
Vast.ai is thrilled to announce the launch of our Serverless offering for GPU workloads, enabling automatic, pay-per-use scaling for AI inference without hyperscaler pricing or fixed infrastructure constraints.

With [Vast.ai Serverless](https://vast.ai/products/serverless), users run inference workloads through a fully serverless API on Vast's globally distributed GPU cloud. That means no manual instance management and no capacity planning. Instead, the platform automatically applies predictive optimization and flexible scaling, with access to a diverse GPU fleet -- so teams can deploy production AI systems that scale when needed and yet stay cost-efficient at all times.

How does this serverless model work in practice? Let's take a closer look!

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### Where GPU Cloud Meets Serverless AI

Vast.ai Serverless provides access to a wide range of GPUs, from RTX-class consumer GPUs to enterprise-grade machines like A100s, H100s, and B200s. Rather than managing individual instances, all you have to do is define performance targets, and Vast handles the rest.

Behind the scenes, Vast.ai continuously benchmarks GPUs across a global network of more than **17,000 GPUs hosted by 1,400+ providers in over 500 locations worldwide**. As workloads run, the platform dynamically selects, provisions, and routes jobs to the most efficient hardware available at that moment.

The key is predictive optimization.

### Predictive Optimization Built for Real-World Workloads

Most serverless platforms react to demand after it appears. Vast.ai Serverless goes further.

Our predictive optimization feature analyzes historical usage patterns, real-time load, and ongoing market benchmarking to anticipate demand before it peaks. Based on those signals, the platform **proactively provisions GPU workers** that balance cost and latency -- ready to activate on demand rather than scrambling to scale once performance has already degraded.

These reserve workers help avoid the laggy cold starts and unpredictable costs common with other GPU serverless offerings. Not to mention having to pay for excess GPU capacity that isn't actively being used.

### Multiple Workergroups per Endpoint

Unlike traditional serverless systems that restrict endpoints to a single hardware profile, Vast.ai Serverless [supports multiple Workergroups per Endpoint](https://docs.vast.ai/documentation/serverless/architecture). A Workergroup is a collection of GPU workers that are managed as a logical unit to automatically adjust capacity to meet demand.

In our Serverless offering, each Workergroup in an Endpoint can specify different GPU types or hardware configurations. This lets a single API endpoint serve workloads using whichever Workergroup is most cost-effective or the best fit for your performance targets. For example, lighter requests might route to consumer GPUs, while heavier inference jobs scale onto H100s -- with no manual intervention needed.

In practice, this makes it possible to optimize performance and cost-efficiency in real time within a single deployment. And that brings us to pricing.

### Lowest-Cost Transparent Serverless Pricing

Vast.ai Serverless is the **lowest-cost autoscaling GPU cloud** on the market today.

Our Serverless workloads are billed per second, with support for On-Demand, Interruptible, and Reserved pricing. There are [no tiers and no limits](https://docs.vast.ai/documentation/serverless/pricing) -- and all you need is $5 to get started.

Because Vast draws from a competitive global market of GPU providers, pricing reflects real supply and demand rather than preset SKUs. As demand scales up, Vast.ai Serverless automatically selects the most cost-effective GPUs available. When demand drops, resources are released immediately, and billing stops.

As a result, teams can run production workloads at a fraction of the cost compared to traditional providers with centralized infrastructure. However, lower costs don't mean lower standards. Security and compliance remain core to our platform.

### Enterprise-Ready Security and Compliance

Vast.ai is built with security and compliance as foundational principles. Our platform is backed by **SOC 2 Type II** [certification](https://vast.ai/article/vast-soc2-typeII-certification), with ongoing audits every 12 months to ensure continual coverage.

For customers with the highest security requirements, Vast.ai offers a tiered compliance structure with our [Secure Cloud](https://cloud.vast.ai/?secure_cloud=true) offering. In the Secure Cloud, workloads run on isolated instances with direct SSH, CLI, and API access, hosted by our vetted datacenter partners that meet **ISO 27001** standards at minimum. Many partners maintain additional certifications. (Full details are available on our [compliance page](https://vast.ai/compliance).)

Other enterprise security features -- including private VPN access, optional audit trails, and enterprise-grade compliance support -- can be enabled as needed.

In all cases, data sovereignty remains fully in your control. Models, data, and workloads persist only as long as you choose and can be deleted at any time.

### How to Automatically Scale GPU Compute for AI Inference

With Vast.ai Serverless, you can go from [zero to compute](https://docs.vast.ai/documentation/serverless) in seconds. In short, here's how the process works:

1. Bring your own machine learning model
2. Send an inference request
3. Vast.ai Serverless routes and provisions compute on ready GPUs
4. GPU executes and returns results
5. Serverless predictively scales up and down based on usage

That's all there is to it! From request to response, Vast.ai handles all of the heavy lifting. You also gain access to ample metrics and debugging tools, including logs and Jupyter/SSH.

### Get Started with Vast.ai Serverless

Our Serverless offering makes it easy to run AI inference without managing infrastructure or overpaying for capacity. Vast.ai Serverless gives you a simple and straightforward path from experiment to production -- and it's ready when you are.

Leverage Vast's distributed GPU fleet on your own terms. Check out our [Serverless Overview](https://docs.vast.ai/documentation/serverless) and start building today!


Announcing Vast.ai Serverless for GPU inference with predictive optimization, multi-workergroup endpoints, pay-per-second pricing, and enterprise-grade compliance.

Vast.ai Serverless: Automated GPU Scaling for AI Inference - Without the Overhead


We're back with a new update from the team at **Vast.ai**\! This month, we've rolled out fresh hardware options, new video-generation tools, and continued improvements behind the scenes.

The big news is that our GPU lineup now includes the **NVIDIA DGX B200** as well as the **RTX PRO 6000 WS**\! These powerful machines open the door to even more demanding workloads, bringing you the compute power required for everything from multi-modal inference to precision-driven simulations.

We also recently reached an exciting milestone: we now have **over one thousand RTX 5090 GPUs available on Vast** – 1,180 and counting.

Here are the details about what's new this month\!

### NVIDIA Cloud GPU Updates

Whether you're training foundation models, rendering high-resolution video, or running inference, our latest hardware additions give you extra flexibility:

-   **NVIDIA DGX B200** – A universal AI supercomputer built on the Blackwell architecture, equipped with **eight GPUs** and **1.4 TB** of GPU memory. With massive bandwidth and compute capacity, it's well suited to handle virtually any enterprise AI workload.

-   **RTX PRO 6000 WS** – A high-end Blackwell-based workstation GPU featuring **96 GB** of GDDR7 memory and up to **4000 TOPS** of AI compute. It can easily handle massive datasets and multi-billion-parameter models while supporting seamless multi-app workflows.

### New Guide and Templates

We've focused on video generation this month, adding a couple of new templates and a guide to help you go from idea to clip as seamlessly as possible:

-   [**Guide to Video Generation**](https://docs.vast.ai/video-generation) – A walkthrough for setting up and using ComfyUI on Vast.ai, so you can create advanced stable diffusion pipelines (ideal for video generation workflows) with this powerful node-based interface.

-   **LTX Video (ComfyUI)** – A ready-to-use template that pairs the LTX Video model with ComfyUI, optimized for consumer-grade GPUs.

-   **Open-Sora** – A template for this open-source video generation model with a streamlined, user-friendly platform that lowers the barrier to efficient, high-quality video content creation.

And that's a wrap for June 2025\!

### Our Commitment

Whether you're looking to launch a B200 instance, experiment with video generation using our latest templates, or scale up with our expanding lineup of RTX 5090 GPUs, we're here to support your work at every stage. Our goal at **Vast.ai** is to make powerful compute accessible, flexible, and easy to use – so you can focus on building, training, and creating.

Need help? Reach out to our customer support team anytime at [support@vast.ai](mailto:support@vast.ai), or connect with the community on our Discord server for tips, troubleshooting, and more.

### Change Log

#### New Templates & Guides:

-   [Guide to Video Generation](https://docs.vast.ai/video-generation)
-   LTX Video (Comfy UI) – Video-Gen model optimized for consumer-grade GPUs.
-   Open-Sora – Democratizing efficient video production for everyone.

#### New GPUs:

-   DGX B200
-   RTX PRO 6000 WS

Plus we now have over one thousand 5090 GPUs available.


New DGX B200 and RTX PRO 6000 WS GPUs, over 1000 RTX 5090s available, and new video generation templates.

June 2025 Product Update


Nearly every startup faces the same two pressures: to move fast and make every dollar count.

Time and budget may be limited while demands remain high – especially for teams working with AI, machine learning, or other compute-heavy workloads. For startups, the pressure to deliver quickly and prove technical viability makes access to reliable infrastructure even more critical.

Traditional cloud providers often aren't built with startups in mind. Pricing may be steep or overly complex, resources can be constrained, and it's easy to end up locked into systems that don't scale with your needs. On the other hand, buying your own hardware adds upfront costs and delays, which can be difficult to justify for early-stage teams.

Vast.ai offers another path. **Our market-based cloud GPU rental platform gives startups the flexibility to run powerful workloads without the usual friction.**

Here's a closer look at why startups choose to build with Vast.ai.

### Why Vast.ai Makes Sense for Startups

Startups need infrastructure that keeps up with their pace, budget, and technical demands. The following are some of the reasons Vast.ai hits the mark.

#### 1\. Instant Access to GPUs

With Vast.ai, there are no waitlists or delays. Startups can instantly access powerful GPUs like **H100s**, **A100s**, and **RTX 5090s**, backed by a global network of providers that ensures better availability and built-in redundancy.

Plus, with machines available around the world, startups can deploy GPUs in regions closer to their major user concentrations, reducing network latency for performance-sensitive applications.

#### 2\. Cost-Effective Compute Power

Vast.ai offers significantly cheaper GPU rental compared to traditional cloud providers – **on average 5-6X lower in cost** – making high-performance compute far more accessible.

For bootstrapped startups and solo founders, Vast.ai levels the playing field, enabling smaller teams on a budget to compete in compute-intensive spaces, as well as handle temporary spikes in workload without having to invest in expensive infrastructure.

#### 3\. Flexible Startup-Friendly Billing

Vast.ai's **pay-as-you-go pricing** gives startups the freedom to scale compute resources up or down as needed, with no lock-in contracts. This way, **you only pay for what you use**, and you can quickly adapt to new demands as they arise.

On-demand and interruptible (bid auction) [rental options](https://docs.vast.ai/instances/rental-types) offer even more control – ideal for variable usage patterns and budget-conscious teams. On top of that, real-time cost tracking and detailed resource utilization dashboards help you monitor and optimize performance.

#### 4\. Enterprise-Grade Security

With Vast.ai, **startups can choose the level of trust and security that best fits their needs.** Providers [range](https://vast.ai/compliance) from verified hosts, suitable for general computing needs, to our vetted datacenter partners who maintain third-party compliance certifications (at minimum ISO 27001 certified) and sign comprehensive Data Processing Agreements (DPAs) with Vast.ai.

For sensitive or production workloads, our Secure Cloud tier ensures you’re connecting with partners in certified locations that are current with ISO 27001 and/or Tier 3/4 standards. (You can even filter for it. Look for the blue datacenter label\!)

We've also [completed](https://vast.ai/article/vast-ai-achieves-soc-2-type-1-certication) our SOC 2 Type I certification – part of our ongoing commitment to the highest standards of regulatory compliance.

#### 5\. Developer Control

Vast.ai provides **transparent instance specs** (GPU type, RAM, storage, and more), so startups can choose exactly what fits their workload and budget. Developers can bring their own Docker images, run their own stack, and configure environments to match their specific requirements.

With a low barrier to entry, even early-stage teams without dedicated DevOps support can get up and running quickly. Our selection of pre-configured [templates](https://docs.vast.ai/templates) are like recipes that help users easily set up instances; templates can define the Docker image, environment variables, and launch mode for each new instance.

#### 6\. Ideal for AI Workloads

Vast.ai is well suited for AI startups working on large-scale training, fine-tuning, and inference. Our customizable, on-demand [CPU/GPU clusters](https://vast.ai/clusters) ensure reliable throughput and flexible resource allocations for continuous or large-scale workloads.

With support for large language models (LLMs) and diffusion models, our platform is compatible with all major machine learning (ML) frameworks and offers the flexibility to run custom containers and tooling.

For teams that want to get started quickly, Vast.ai provides **ready-to-use GPU instances pre-configured for frameworks like TensorFlow and PyTorch**, reducing setup time and simplifying deployment.

#### 7\. Built to Experiment

With Vast.ai, **startups can prototype, test, and explore without any long-term commitments** or expensive upfront investment.

For instance, teams can run quick tests and train models on demand, access and evaluate cutting-edge GPUs like the H200 that may otherwise be out of reach, and even spin up high-performance environments on short notice for hackathons and demos.

### The Bottom Line

**Great ideas can come from anywhere, and they deserve the resources to grow.** By making GPU compute power more open and accessible, Vast.ai gives startups and early-stage teams a real shot at achieving their goals.

Built with transparency, accessibility, and security in mind – and aligned with the open-source ethos that many founders value – our platform supports the kind of flexibility and control that your team needs from day one.

Ready to build, test, and scale on your terms? Discover what's possible with Vast.ai\!


Vast.ai offers startups instant, low-cost access to powerful GPUs—perfect for AI, ML, and compute-heavy work—without lock-ins or upfront infrastructure costs.

7 Reasons Why Startups Use Vast.ai


A new NVIDIA Blackwell GPU with 96GB of VRAM? If rumors are to be believed, that could be exactly what's coming. 

A recent shipping manifest [revealed](https://wccftech.com/nvidia-rtx-pro-6000-x-blackwell-leak-96-gb-gddr7-600w/) an entry for the "**RTX PRO 6000 X Blackwell**," evidently being sent to India "for testing purposes." At least two different shippers have sent the GPUs for testing.

This isn't your typical consumer-grade gaming card, however. The as-yet-unannounced Blackwell-based GPU appears to be aimed at AI and data-intensive workloads. Here's what we know so far about this new entry in NVIDIA's professional lineup.

### NVIDIA RTX Pro 6000 X: What We Know So Far

With the impressive [RTX 5090](https://vast.ai/article/nvidia-geforce-rtx-5090-specs-everything-you-need-to-know) expected to remain at the top of the GeForce 50-series lineup, the professional segment is where we'll likely see ultra-high-end specs that push the limits on VRAM – and this latest shipping log discovery suggests that will be the case. 

According to reports, as mentioned, the RTX Pro 6000 X features a staggering **96GB of GDDR7 memory** on a 512-bit memory bus. That's significantly higher than even the most powerful gaming cards. For example, the RTX 5090 has 32GB of GDDR7 memory – which is more than enough for most purposes – but higher VRAM is obviously beneficial for workloads that require handling massive datasets, such as foundation AI model training and large-scale simulations.

The RTX Pro 6000 X also features a **GB202 die** and **600 watts** of total board power (TBP). This aligns with previous high-end Blackwell architecture designs, like the RTX 5090, which also has the GB202 die and comes in at 575 watts of TBP.

Interestingly, the discovery of the RTX Pro 6000 X on the shipping manifest follows an [earlier sighting](https://wccftech.com/nvidia-rtx-4090-with-96gb-vram-reportedly-exists/) of a 96GB RTX 4090 GPU – perhaps an engineering sample. While NVIDIA hasn't made any announcements, these repeated sightings nonetheless suggest a broader push for high-VRAM GPUs.

### Comparison to Existing RTX Workstation GPUs

We've seen professional-grade GPUs with massive VRAM configurations before, such as the RTX 6000 Ada with 48GB of memory, as well as the A100/A800 series. However, the RTX Pro 6000 X's 96GB of GDDR7 memory would make it one of the highest-capacity workstation GPUs to date. 

Assuming we see the RTX Pro 6000 X emerge, it would set a new benchmark for professional GPUs, doubling the memory of the RTX 6000 Ada while offering the faster speeds and efficiency of GDDR7 memory.

Plus, unlike the A100 and A800 series, which are designed primarily for data centers and enterprise AI workloads, the RTX Pro 6000 X could serve as a more flexible solution. Its ultra-high VRAM would allow professionals to work with larger datasets without necessarily relying on multi-GPU setups. 

### Looking Ahead

NVIDIA hasn't officially announced the RTX Pro 6000 X, so all information should be taken with a grain of salt, but the repeated mentions of this new GPU in shipping logs suggest that an announcement may be imminent. 

For Vast.ai users, this could mean more powerful GPU rental options in the near future. Stay tuned for more developments\! In the meantime, check out our current selection of high-performance GPUs available on Vast.ai for flexible and cost-effective access to cutting-edge hardware today.  

NVIDIA’s rumored RTX Pro 6000 X packs 96GB of GDDR7 VRAM, a GB202 die, and 600W TBP—ideal for AI, data science, and high-end workstation workloads.

NVIDIA RTX Pro 6000 X Rumors: Blackwell GPU with 96GB VRAM Spotted in Shipping Log


The growth of AI is outpacing compute. Today, there's an unprecedented demand for computing power as interest in AI surges worldwide – and servers just can't keep up.

More advanced AI models require increasingly powerful hardware. **Without enough GPUs, these large-scale AI systems struggle to run, and also cannot improve.**

Even OpenAI's CEO, Sam Altman, has [admitted](https://techcrunch.com/2024/10/31/openai-ceo-sam-altman-says-lack-of-compute-is-delaying-the-companys-products/) that computing limitations are slowing down the company's ability to release new products. Microsoft's CFO, Amy Woods, [noted](https://www.microsoft.com/en-us/Investor/events/FY-2025/earnings-fy-2025-q1) last year that demand for AI "continues to be higher" than their "available capacity."

If industry giants like OpenAI and Microsoft are feeling the strain, smaller organizations face even steeper challenges. Without deep pockets or dedicated data centers, they often have no choice but to explore other options.

With GPUs in short supply and demand skyrocketing, many companies are turning to a more flexible, scalable solution: **GPU-as-a-service, or GPUaaS**.

## What Is GPU-as-a-Service?

Simply put, **GPUaaS is a cloud-based solution that provides on-demand access to high-performance GPUs.** It fills a gap in the AI industry by enabling customers to rent the compute power they need, scaling up or down as their workloads demand.

Big cloud providers like AWS, Google Cloud, and Azure offer GPU instances, but GPUaaS goes further. Platforms like our own here at Vast.ai tap into a decentralized network of idle GPUs from data centers, enterprises, and even individuals, making high-performance compute more affordable and widely available.

As a result, GPUaaS addresses a critical need: namely, that **AI infrastructure must be scalable, cost-efficient, and accessible to everyone.**

## Why GPUaaS Is the Key to Scalable AI Infrastructure

AI adoption shows no sign of slowing down – and with it comes fluctuating and unpredictable demand for computing power at any given time. Traditional centralized infrastructure isn't well suited to this type of volatility.

Relying on centralized GPU resources often leads to wasted potential and frustrating roadblocks, such as:

-   **High upfront costs** for purchasing and maintaining GPUs.
-   **Underutilized hardware** during slow periods, resulting in inefficiency and wasted costs.
-   **Bottlenecks** when demand spikes, slowing down innovation and delaying results.
-   **Limited access** to GPUs due to supply chain slowdowns and shortages, impacting AI scalability.

This is where GPU-as-a-Service offers a smarter solution. With GPUaaS, you can:

-   **Quickly scale AI workloads** to match demand without over-provisioning.
-   **Reduce costs** with a flexible pay-as-you-go model while eliminating the risks of heavy upfront investment.
-   **Access worldwide GPU resources**, minimizing latency and enhancing performance.
-   **Decrease energy consumption** by leveraging existing, idle GPUs instead of powering additional servers.

## GPUaaS: The Future of AI Compute is Flexible

As AI continues to advance, organizations that can efficiently scale their compute power will have a clear advantage. GPU-as-a-Service reduces the barriers of cost, accessibility, and scalability, offering an alternative to traditional infrastructure. With GPUaaS, you can tap into a global network of GPUs and ensure you always have the resources you need, when you need them.

So why wait? In a world where AI innovation moves fast, compute should never be the bottleneck. **Vast.ai makes enterprise-grade GPUs accessible and affordable through our decentralized marketplace, offering savings of 5-6X on GPU compute.** Whether you're training cutting-edge models or running large-scale inference, we provide the flexibility and efficiency to keep your AI projects moving forward.

Get started and harness the power of GPUaaS with Vast.ai today!

GPU shortages are slowing AI growth. GPU-as-a-Service (GPUaaS) offers a scalable, cost-effective solution for on-demand AI compute power.

GPU-as-a-Service: The Scalable Solution to AI's Compute Crisis


# 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.

Posts about: Gpu

Vast.ai Serverless: Automated GPU Scaling for AI Inference - Without the Overhead

June 2025 Product Update

7 Reasons Why Startups Use Vast.ai

NVIDIA RTX Pro 6000 X Rumors: Blackwell GPU with 96GB VRAM Spotted in Shipping Log

GPU-as-a-Service: The Scalable Solution to AI's Compute Crisis

NVIDIA GeForce RTX 5090: Release Announced