
When you're training a custom AI model, access to the right GPU compute resources is crucial. That can be a tall order – but you don't have to drain your budget or get locked into long-term contracts to get the compute power you need. With **Vast.ai**, there's a better way.

Our cloud GPU rental platform gives you direct access to high-performance GPUs through a transparent, pay-as-you-go model that lets you train on your own terms: anytime, anywhere, and at the right price.

In this post, we'll take a closer look at how Vast.ai helps you train custom AI models faster and more affordably, without compromising performance or scalability.

## 1. Flexible, Budget-Friendly Pricing

Training AI models can get expensive. Vast.ai offers different [pricing options](https://docs.vast.ai/instances/rental-types) to help you keep cloud costs under control:

**Interruptible instances** let you rent GPUs at lower prices through interruptible or bid-based access. While these instances can be interrupted with little notice, they're a great fit for workloads that can handle brief disruptions in exchange for major cost savings – **up to 80% cheaper** than traditional cloud rates. For many users, that tradeoff is worth it for the reduction in training costs.

**On-demand instances** give you uninterrupted GPU access at a fixed rate. This setup is best for continuous training, real-time inference, or workloads where stability is crucial.

Of course, price is just one part of the equation: the next step is choosing the right GPU for the job. Vast.ai gives you that flexibility, as well.

## 2. GPU Options for Every AI Workload

There's no one-size-fits-all solution when you're training custom AI models. That's why our wide range of cloud GPU options is such an advantage. You can choose exactly what your workload needs, whether that's an enterprise-grade accelerator like the **NVIDIA [A100](https://vast.ai/article/H100-vs-A100-Comparing-two-Powerhouse-GPUs)** or **[H200](https://vast.ai/article/nvidia-h100-vs-h200-two-hopper-based-heavyweights)**, an ultra-high-performance machine like the **[B200](https://vast.ai/article/nvidia-h200-vs-b200-comparing-datacenter-grade-accelerators)**, or a top-tier consumer card like the **[RTX 4090](https://vast.ai/article/nvidia-rtx-4090-vs-a100-two-powerhouses-two-purposes)**, **[5090](https://vast.ai/article/nvidia-geforce-rtx-5090-specs-everything-you-need-to-know)**, or **[L40S](https://vast.ai/article/comparing-nvidia-l40-vs-l40s-and-more)**.

Each listing on Vast.ai displays **transparent instance specs**, making it easy to compare GPUs by type, RAM, storage, location, and more. Simply filter and select the best fit for you!

Most importantly, you'll never have to worry about overpaying for capacity you don't need or being held back by hardware limitations. When your workloads grow, scaling up is seamless. Vast.ai lets you spin up multiple GPU instances and customizable, on-demand [GPU/CPU clusters](https://vast.ai/clusters), so you can expand training capacity instantly – and only pay for what you use.

## 3. Supporting Real-World Use Cases

Our platform is ideal for anyone training, fine-tuning, or experimenting with custom AI models. Whether you're an AI researcher or developer, an enterprise team, or a [startup](https://vast.ai/article/7-reason-why-startups-uses-vast-ai), Vast.ai provides the compute power you need to train models for use cases like automating customer inquiries, identifying sales trends and other business insights, and analyzing massive research datasets.

You can build and refine domain-specific models, fine-tune foundation models like Llama and Mistral on proprietary data, and retrain as your datasets evolve. With support for LLMs and diffusion models, our platform is **compatible with all major machine learning (ML) frameworks** – and you can even run custom containers and tooling.

## 4. Quick Start with Pre-Built Templates

You don't have to start from a blank slate. Vast.ai offers pre-built [templates](https://docs.vast.ai/templates) for popular frameworks – **PyTorch, TensorFlow, vLLM, ComfyUI, and more** – that are designed to help you launch a training environment in minutes.

Each template includes the dependencies and configurations you need for model training or fine-tuning, so you can spend less time setting up and more time improving your results. Customize templates with your own datasets, scripts, or containers. You have complete flexibility to tailor them to your project needs!

Finally, Vast.ai also ensures that you can train your AI models in a trusted environment [with control over data exposure](https://vast.ai/article/running-private-ai-models-without-the-risk-of-data-exposure).

## 5. Enterprise-Grade Security

With Vast.ai, your workloads run on a secure, trusted infrastructure designed to [protect your data](https://vast.ai/compliance) – crucial when training custom AI models. Choose either **verified hosts** with strong reliability metrics or **vetted datacenter partners** who maintain third-party compliance certifications and are bound by comprehensive Data Processing Agreements (DPAs) with Vast.ai.

Our **Secure Cloud** offering gives you the option to select GPU infrastructure only from our vetted datacenter partners. Each of these providers is located in a certified facility that's current with ISO 27001 and/or Tier 3/4 standards at minimum.

We've also [completed](https://vast.ai/article/vast-soc2-typeII-certification) our own SOC 2 Type II certification, affirming our adherence to industry-leading compliance frameworks.

## Get Started with Vast.ai

Training custom AI models shouldn't be limited to those with deep pockets or powerful infrastructure. **Vast.ai** levels the playing field.

With high-performance GPUs, flexible pricing, and pre-configured templates to help you get started, you can move from idea to implementation faster and more affordably than ever with Vast.ai. You'll also enjoy peace of mind knowing that your data and models are protected by enterprise-grade security every step of the way.

Ready to train your own model? Spin up your first instance today at Vast.ai!


When you're training a custom AI model, access to the right GPU compute resources is crucial. Vast.ai gives you direct access to high-performance GPUs through a transparent, pay-as-you-go model that lets you train on your own terms.

Why Choose Vast.ai to Train Custom AI Models


We're excited to announce a new deployment guide for MiniMax-M2, the breakthrough 230 billion parameter language model that's making waves in the open-source AI community. If you've been looking for a cost-effective way to run state-of-the-art LLMs, this guide is for you.

## What Makes MiniMax-M2 Special?

MiniMax-M2 achieves the #1 composite score among open-source models while maintaining incredible efficiency. Despite its 230B parameter size, it activates only 10B parameters per inference, delivering fast responses without the typical computational overhead of large models.

Key features include:
- **Interleaved Thinking**: Transparent reasoning via `<think>...</think>` tags
- **131K Token Context**: Handle large documents and complex conversations
- **OpenAI Compatible**: Drop-in replacement for existing applications
- **MIT Licensed**: No restrictions on commercial use

## What's in the New Guide

Our latest documentation example walks you through deploying MiniMax-M2 on Vast.ai from start to finish. The guide includes:

**Complete Deployment Instructions**
- Hardware requirements and instance selection
- Step-by-step provisioning with the Vast.ai CLI
- API key authentication for secure deployments
- Configuration parameters validated through real deployments
- Expected timelines and what to watch for

**API Integration Examples**
- Python code using the OpenAI SDK with authentication
- cURL examples for quick testing
- Streaming response implementations
- Best practices for multi-turn conversations

**Troubleshooting Section**
Based on real deployment testing, we've documented the four critical issues you might encounter and their solutions:
- Docker image version requirements
- Disk space allocation
- GPU memory optimization
- CUDA driver compatibility

**Performance Data**
All metrics in the guide come from actual deployments on Vast.ai infrastructure:
- 30-minute initial deployment time
- ~7 seconds for 100-token responses
- 131K token context window
- Production-ready performance on 4x H100 GPUs

## Why Deploy on Vast.ai?

Vast.ai's GPU marketplace gives you access to enterprise hardware at competitive rates, making it economical to run powerful models like MiniMax-M2 for production workloads. Our guide uses a 4x H100 (80GB) configuration with the vLLM nightly build. You get:

- Enterprise GPU access (H100 recommended for best compatibility)
- Predictable costs with no usage-based pricing
- Full control over your infrastructure
- OpenAI-compatible API for easy integration

The guide shows you how to leverage these advantages for cost-effective LLM inference that rivals cloud API quality.

## Scaling for Production

While the 4x H100 configuration demonstrated in our guide provides an excellent starting point for deploying and testing MiniMax-M2 on Vast.ai, production deployments typically require larger GPU configurations to support longer context lengths and higher concurrent request volumes. For production use cases, consider configurations such as 8x H100, 4x H200, or 8x H200, which provide substantially more GPU memory for handling concurrent requests and extended context windows.

## Who Should Use This Guide?

This deployment guide is perfect for:
- **Developers** building AI-powered applications who want cost control and flexibility
- **Research teams** running experiments that need high-volume inference
- **Startups** prototyping AI features without committing to expensive cloud APIs
- **Anyone** curious about deploying open-source LLMs at scale

## Get Started

The complete guide is now available in our documentation:

**[Read: Running MiniMax-M2 on Vast.ai →](https://docs.vast.ai/examples/text-generation/minimax-m2#minimax-m2)**

Whether you're exploring open-source AI options or ready to deploy your first large language model, this guide provides everything you need to get MiniMax-M2 running on Vast.ai infrastructure.

Ready to try it? [Sign up for Vast.ai](https://cloud.vast.ai) and follow the guide to deploy your first instance.

---

*All performance metrics and cost estimates in this article are based on actual deployment testing on Vast.ai infrastructure. Results may vary based on instance availability and configuration.*


Learn how to deploy MiniMax-M2, the 230B parameter language model with state-of-the-art performance, on Vast.ai infrastructure.

New Guide: Deploy MiniMax-M2 on Vast.ai

	
Choosing an RTX 6000 GPU isn't quite as straightforward as it might seem. The name "RTX 6000" now spans multiple cards across two architectures – the NVIDIA RTX 6000 Ada, as well as three newer Blackwell-based variants built for very different workloads. The four GPUs share some impressive fundamentals, but their performance profiles and a few important design differences set them apart.

Today we're looking at these NVIDIA RTX 6000 GPU options, what they're built for, and how to decide which one best fits your needs. So let's get into it!

### The RTX 6000 Family at a Glance

For starters, here's a brief overview of the four GPU models in the RTX 6000 lineup:

-   [RTX 6000 Ada Generation](https://cloud.vast.ai/?gpuModelNames=rtx6000AdaGeneration&instanceDiskSizeMin=700&instanceDurationMin=604800&instanceType=onDemand&isAvxSupported=false&isHostSecure=false&isMachineIpStatic=false&isOfferAvailable=true&isOfferCompatible=true&isOfferVerified=true&isQueryInverted=false&machineReliabilityMin=0.9&sorts=score-desc) – An enthusiast-class professional graphics card with solid all-around performance.

-   [RTX PRO 6000 Blackwell Workstation Edition](https://cloud.vast.ai/?gpuModelNames=rtxPro6000BlackwellWorkstation&instanceDiskSizeMin=700&instanceDurationMin=604800&instanceType=onDemand&isAvxSupported=false&isHostSecure=false&isMachineIpStatic=false&isOfferAvailable=true&isOfferCompatible=true&isOfferVerified=true&isQueryInverted=false&machineReliabilityMin=0.9&sorts=score-desc) – A full-power 600W desktop card, best used for single-GPU high-throughput workflows.

-   **RTX PRO 6000 Blackwell Max-Q Workstation Edition** – Basically the same hardware as the standard Workstation Edition above, but with a few design differences that make it better suited for multi-GPU setups.

-   [RTX PRO 6000 Blackwell Server Edition](https://cloud.vast.ai/?gpuModelNames=rtxPro6000BlackwellServer&instanceDiskSizeMin=700&instanceDurationMin=604800&instanceType=onDemand&isAvxSupported=false&isHostSecure=false&isMachineIpStatic=false&isOfferAvailable=true&isOfferCompatible=true&isOfferVerified=true&isQueryInverted=false&machineReliabilityMin=0.9&sorts=score-desc) – A passive-cooled variant designed for rackmount systems and high-density environments.

Next, we'll break down each GPU in more detail, then wrap up with some benchmarks and a side-by-side comparison table to help guide your decision.

### RTX 6000 Ada Generation: Solid and Dependable

The RTX 6000 Ada is the previous-generation flagship graphics card, and it still holds its own as a solid, versatile workstation GPU. Built on the Ada Lovelace architecture, it delivers strong performance for enhanced content creation and light-to-moderate AI training.

Overall, it's a well-rounded option. Featuring 48GB of GDDR6 ECC memory, a respectable 18,176 CUDA cores, up to 91.1 TFLOPS of single-precision performance, and 300W TDP, the RTX 6000 Ada can handle complex tasks and multi-application workflows without the more extreme price or power demands of a Blackwell-class GPU. It remains dependable for many professional use cases, especially if your projects don't require cutting-edge AI acceleration.

The RTX 6000 Ada Generation may be the right choice for you if:

Your workloads span creative applications, 3D tools, and mixed GPU tasks.

You don't need 96GB of VRAM but still want professional-grade stability and performance.

You're looking for a more cost-effective alternative to Blackwell for workstation use.

### RTX PRO 6000 Blackwell Workstation: Top-Tier Desktop Performance

The RTX PRO 6000 Blackwell Workstation Edition is the top-performing desktop card in this group. Built on NVIDIA's Blackwell architecture, it boasts 96GB of GDDR7 memory, a wide 512-bit memory bus, and extremely high memory bandwidth at nearly 1.8 TB/s. Plus, it has a staggering 125 TFLOPS of single-precision performance. This combination makes it well suited for the most demanding AI workloads as well as complex visualization, simulation, and neural rendering tasks.

The Workstation Edition can sustain peak performance under a 600W power load, without thermal throttling – but at the same time, it's not recommended for dense multi-GPU configurations due to its higher heat output and flow-through dual-fan design. Instead, it works best as a single GPU in a workstation setup.

The RTX PRO 6000 Blackwell Workstation Edition makes sense for you if:

You want the highest sustained throughput the RTX PRO 6000 series can deliver in a workstation, for the most demanding workloads.

You don't need to run multiple GPUs in the same system.

Your workstation has the power and cooling to support a 600W GPU.

### RTX PRO 6000 Blackwell Max-Q Workstation: Multi-GPU Friendly

RTX PRO 6000 Blackwell Max-Q Workstation Edition is ideal for anyone who wants Blackwell-level performance in a cooler, more efficient card that works well in multi-GPU configurations. For simplicity's sake, let's call it the Max-Q variant as opposed to the standard Workstation Edition.

The Max-Q features the same basic hardware as the full-power standard Workstation Edition – including 96GB of GDDR7 memory, a 512-bit bus, and 1.8 TB/s memory bandwidth – but it runs at a reduced 300W TDP and uses a blower-style cooler. Its enclosed single-fan design pulls air in and then exhausts it out the rear of the chassis. With lower heat output and superior management of airflow, the Max-Q is a much better fit for multi-GPU configurations than the standard Workstation Edition.

You may want to go with the Max-Q Workstation model if:

You're looking for Blackwell-class performance and memory capacity in a cooler, more efficient card.

You can't support a 600W GPU but still need 96GB of VRAM and high bandwidth.

You plan to run 2-4 GPUs and your workloads benefit from multi-GPU scaling.

### RTX PRO 6000 Blackwell Server Edition: Scalable and Server-Ready

The RTX PRO 6000 Blackwell Server Edition is built specifically for datacenter and rackmount environments. It lacks an active cooling fan, instead relying entirely on external airflow with a passive heatsink. It can also be configured up to the full 600W power envelope – as long as your airflow solution can handle the cooling required, of course!

Like the other Blackwell variants, the Server Edition includes 96GB of GDDR7 memory and a 512-bit bus. Its memory bandwidth is slightly lower at 1.6 TB/s, compared to 1.8 TB/s on the workstation cards, but the Server Edition is still more than capable of handling large models, massive datasets, and multi-GPU workloads. What really sets it apart is its ability to deliver stable, scalable performance in high-density environments.

Choose the RTX PRO 6000 Blackwell Server Edition if:

You need stable performance for continuous workloads across large-scale multi-GPU setups.

You want Blackwell-level memory and bandwidth in a form factor designed for dense compute.

You're deploying into rackmount servers where the chassis provides adequate high-pressure airflow.

### RTX 6000 Series: Performance Benchmarks

Now that we've covered design differences and use cases, let's check out how these GPUs actually perform. The benchmarks below show how each RTX 6000 variant handles real workloads, so you can get a clearer picture of what to expect in practice.

![RTX 6000 benchmark comparison](/uploads/vast_blog_6000_comparison_benchmarks.png)

And finally, for reference, here's a side-by-side specs comparison table showing how the four GPUs stack up against each other:

<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 6000 Ada
      </th>
      <th class="border border-gray-300 px-4 py-2 text-center font-semibold">
        RTX PRO 6000 Blackwell Workstation
      </th>
      <th class="border border-gray-300 px-4 py-2 text-center font-semibold">
        RTX PRO 6000 Max-Q
      </th>
      <th class="border border-gray-300 px-4 py-2 text-center font-semibold">
        RTX PRO Blackwell Server Edition
      </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 text-center">Blackwell</td>
      <td class="border border-gray-300 px-4 py-2 text-center">Blackwell</td>
      <td class="border border-gray-300 px-4 py-2 text-center">Blackwell</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">48GB GDDR6</td>
      <td class="border border-gray-300 px-4 py-2 text-center">96GB GDDR7</td>
      <td class="border border-gray-300 px-4 py-2 text-center">96GB GDDR7</td>
      <td class="border border-gray-300 px-4 py-2 text-center">96 GB GDDR7</td>
    </tr>
    <tr>
      <td class="border border-gray-300 px-4 py-2 font-semibold">
        ECC
      </td>
      <td class="border border-gray-300 px-4 py-2 text-center">Yes</td>
      <td class="border border-gray-300 px-4 py-2 text-center">Yes</td>
      <td class="border border-gray-300 px-4 py-2 text-center">Yes</td>
      <td class="border border-gray-300 px-4 py-2 text-center">Yes</td>
    </tr>
    <tr class="bg-gray-50">
      <td class="border border-gray-300 px-4 py-2 font-semibold">
        Memory Bandwidth
      </td>
      <td class="border border-gray-300 px-4 py-2 text-center">960 GB/s</td>
      <td class="border border-gray-300 px-4 py-2 text-center">1,792 GB/s</td>
      <td class="border border-gray-300 px-4 py-2 text-center">1,792 GB/s</td>
      <td class="border border-gray-300 px-4 py-2 text-center">1,597 GB/s</td>
    </tr>
    <tr>
      <td class="border border-gray-300 px-4 py-2 font-semibold">
        Memory Interface
      </td>
      <td class="border border-gray-300 px-4 py-2 text-center">384-bit</td>
      <td class="border border-gray-300 px-4 py-2 text-center">512-bit</td>
      <td class="border border-gray-300 px-4 py-2 text-center">512-bit</td>
      <td class="border border-gray-300 px-4 py-2 text-center">512-bit</td>
    </tr>
    <tr class="bg-gray-50">
      <td class="border border-gray-300 px-4 py-2 font-semibold">
        CUDA Cores
      </td>
      <td class="border border-gray-300 px-4 py-2 text-center">18,176</td>
      <td class="border border-gray-300 px-4 py-2 text-center">24,064</td>
      <td class="border border-gray-300 px-4 py-2 text-center">24,064</td>
      <td class="border border-gray-300 px-4 py-2 text-center">24,064</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">568 (4th gen)</td>
      <td class="border border-gray-300 px-4 py-2 text-center">752 (5th gen)</td>
      <td class="border border-gray-300 px-4 py-2 text-center">752 (5th gen)</td>
      <td class="border border-gray-300 px-4 py-2 text-center">752 (5th gen)</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">142 (3rd gen)</td>
      <td class="border border-gray-300 px-4 py-2 text-center">188 (4th gen)</td>
      <td class="border border-gray-300 px-4 py-2 text-center">188 (4th gen)</td>
      <td class="border border-gray-300 px-4 py-2 text-center">188 (4th gen)</td>
    </tr>
    <tr>
      <td class="border border-gray-300 px-4 py-2 font-semibold">
        Single-Precision Performance
      </td>
      <td class="border border-gray-300 px-4 py-2 text-center">91.1 TFLOPS</td>
      <td class="border border-gray-300 px-4 py-2 text-center">125 TFLOPS</td>
      <td class="border border-gray-300 px-4 py-2 text-center">110 TFLOPS</td>
      <td class="border border-gray-300 px-4 py-2 text-center">120 TFLOPS (FP32)</td>
    </tr>
    <tr class="bg-gray-50">
      <td class="border border-gray-300 px-4 py-2 font-semibold">
        RT Core Performance
      </td>
      <td class="border border-gray-300 px-4 py-2 text-center">210.6 TFLOPS</td>
      <td class="border border-gray-300 px-4 py-2 text-center">380 TFLOPS</td>
      <td class="border border-gray-300 px-4 py-2 text-center">333 TFLOPS</td>
      <td class="border border-gray-300 px-4 py-2 text-center">355 TFLOPS</td>
    </tr>
    <tr>
      <td class="border border-gray-300 px-4 py-2 font-semibold">
        Total Board Power
      </td>
      <td class="border border-gray-300 px-4 py-2 text-center">300W</td>
      <td class="border border-gray-300 px-4 py-2 text-center">600W</td>
      <td class="border border-gray-300 px-4 py-2 text-center">300W (power-optimized)</td>
      <td class="border border-gray-300 px-4 py-2 text-center">400–600W (configurable)</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">4.4" H x 10.5" L, dual slot</td>
      <td class="border border-gray-300 px-4 py-2 text-center">5.4" H × 12" L, dual-slot, extended height</td>
      <td class="border border-gray-300 px-4 py-2 text-center">4.4" H × 10.5" L, dual-slot</td>
      <td class="border border-gray-300 px-4 py-2 text-center">4.4" H × 10.5" L, dual-slot</td>
    </tr>
    <tr>
      <td class="border border-gray-300 px-4 py-2 font-semibold">
        Thermal (Fan Configuration)
      </td>
      <td class="border border-gray-300 px-4 py-2 text-center">Single-fan active</td>
      <td class="border border-gray-300 px-4 py-2 text-center">Dual-fan active</td>
      <td class="border border-gray-300 px-4 py-2 text-center">Single-fan active</td>
      <td class="border border-gray-300 px-4 py-2 text-center">No-fan passive (server chassis)</td>
    </tr>
    <tr class="bg-gray-50">
      <td class="border border-gray-300 px-4 py-2 font-semibold">
        Date Released
      </td>
      <td class="border border-gray-300 px-4 py-2 text-center">Dec. 2022</td>
      <td class="border border-gray-300 px-4 py-2 text-center">Mar. 2025</td>
      <td class="border border-gray-300 px-4 py-2 text-center">Mar. 2025</td>
      <td class="border border-gray-300 px-4 py-2 text-center">Mar. 2025</td>
    </tr>
  </tbody>
</table>

### Final Thoughts

Each RTX 6000 variant brings something different to the table. As we explored above, the right choice ultimately comes down to how you plan to use it.

To sum things up, the Ada model is solid and dependable for a broad range of workloads, while the full-power Blackwell Workstation card offers the strongest single-GPU performance. The Max-Q variant is the more practical choice for lower-power or multi-GPU setups, and the Server Edition is designed for dense deployments and continuous workloads.

At the same time, these GPUs aren't exactly budget-friendly. That's where Vast.ai comes in. Our cloud GPU rental platform allows you to access this level of performance on demand, eliminating the upfront cost of purchasing the hardware yourself.

With Vast, you can easily match your compute to your workload and only pay for what you use. Best of all, you'll save 5-6X on average compared to traditional cloud providers.

Ready to see what Vast.ai can do for you? Spin up an RTX 6000 GPU instance today, or explore other powerful GPU options like the A100, H200, and B200 – available right now!


A practical guide to every RTX 6000 option, from Ada to the newest Blackwell workstation and server GPUs.

Which NVIDIA RTX 6000 Is Right for You?


In the high-end GPU market, NVIDIA's H100 accelerator family stands out as some of the most advanced hardware available today. Whether you're training AI models or deploying them, performing scientific simulations, or doing large-scale data analytics, the H100 lineup definitely has the performance you need for enormous workloads.

But which accelerator is right for you? Today we're looking at two options: the NVIDIA H100 NVL and the H100 SXM5. Let's dive into the differences and explore which one might be the better fit for your project needs.

## The H100 NVL: Built for AI Inference at Scale

Announced last year, the NVIDIA H100 NVL is an interesting variant on the H100 PCIe card. At first glance, it's exactly what it looks like: two H100 PCIe cards that come already bridged together.

This massive beast of a card spans four slots – with a TDP of 700W to match its size. Given that it's essentially just two H100s glued together, communication is handled by a pair of PCIe 5.0 x16 slots. It uses three NVLink bridges to connect the pair of H100 GPUs, which deliver 600GB/s of bidirectional bandwidth, or about 4.5x the bandwidth of the dual PCIe interfaces.

NVIDIA states that the H100 NVL is capable of 2.4x to 2.6x the performance of two separate H100s (at least when it comes to FP8 and FP16 workloads) – likely in part because it uses faster HBM3 memory rather than HBM2e. Plus, unlike the standard H100 PCIe GPUs, each card on the H100 NVL has all six stacks of HBM memory enabled instead of just five. The result is that the H100 NVL offers a 17.5% memory increase – and a total of 188GB VRAM (2 x 94GB).

A few other standout features include an HBM3 memory clock at ~5.1Gbps; 6144-bit memory bus width; and memory bandwidth of 2 x 3.9TB/s.

According to NVIDIA, the H100 NVL is ideal for inferencing large language models (LLMs):

"These GPUs work as one to deploy large language models and GPT models from anywhere from five billion parameters to 200 [billion]," [explained](https://www.theregister.com/2023/03/21/nvidia_h100_l4/) Ian Buck, NVIDIA's VP of Accelerated Computing.

So in addition to being able to handle various high-performance computing (HPC) tasks, the H100 NVL is specifically optimized for deploying already-trained neural networks at scale.

## The H100 SXM5: Heavyweight for AI Training

SXM technology is a form factor and interconnect standard primarily used for GPUs in large-scale AI applications and data center environments. Instead of relying on PCIe slots like traditional GPUs, SXM GPUs are directly socketed into the motherboard. This allows for faster, high-bandwidth connections as well as better power delivery and cooling solutions – crucial for high-end GPUs, particularly in dense server environments.

Ultimately, this setup helps ensure that SXM GPUs perform efficiently even under the most intense workloads. For instance, NVIDIA's H100 SXM5 is designed for AI training first and foremost – a powerhouse GPU that is especially well suited to training foundational models.

With 528 tensor cores, 80 GB of HBM3 memory, and a memory bandwidth of 3.35 TB/s, the SXM5 is designed for the most demanding machine learning and deep learning tasks. It also benefits from NVLink interconnects that allow up to 900 GB/s of GPU-to-GPU bandwidth, making it ideal for multi-GPU configurations in large-scale AI model training.

Like the H100 NVL, the SXM5 features HBM3 memory. However, the SXM5’s form factor is a bit more restrictive. With each unit pulling 700W, running these systems requires a lot of power and cooling, which can pose a challenge for some datacenters. Most colocation racks have a power capacity of 6-10KW, meaning systems with four or more SXM5s can push infrastructure to its limits.

(The 700W H100 NVL is comparatively easier to accommodate. A single socket, dual H100 NVL configuration – with four GH100 dies – would likely require around 2.5KW.)

A direct comparison of specs would be helpful in deciding which accelerator best suits your needs, especially when balancing power efficiency, infrastructure requirements, and the performance demands of your workloads.

## Which GPU is Right for You?

Here's an outline of some of the features and specs of the H100 NVL and the SXM5, as well as the H100 PCIe for good measure:

<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">
                H100 NVL
            </th>
            <th class="border border-gray-300 px-4 py-2 text-center font-semibold">
                H100 PCIe
            </th>
            <th class="border border-gray-300 px-4 py-2 text-center font-semibold">
                H100 SXM5
            </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">Hopper</td>
            <td class="border border-gray-300 px-4 py-2 text-center">Hopper</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">
                Memory Clock
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                ~5.1 Gbps HBM3
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                3.2 Gbps HBM2e
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                5.23 Gbps HBM3
            </td>
        </tr>
        <tr>
            <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">
                6144-bit
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                5120-bit
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                5120-bit
            </td>
        </tr>
        <tr class="bg-gray-50">
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Memory Bandwidth
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                2 x 3.9 TB/s
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">2 TB/s</td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                3.35 TB/s
            </td>
        </tr>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                FP32 CUDA Cores
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                2 x 16896
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">14592</td>
            <td class="border border-gray-300 px-4 py-2 text-center">16896</td>
        </tr>
        <tr class="bg-gray-50">
            <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">
                2 x 528
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">456</td>
            <td class="border border-gray-300 px-4 py-2 text-center">528</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">
                1.98 GHz
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                1.75 GHz
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                1.98 GHz
            </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">
                2 x 94 GB (188 GB)
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">80 GB</td>
            <td class="border border-gray-300 px-4 py-2 text-center">80 GB</td>
        </tr>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                INT8 Tensor
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                2 x 1980 TOPS
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                1513 TOPS
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                1980 TOPS
            </td>
        </tr>
        <tr class="bg-gray-50">
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                FP16 Tensor
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                2 x 990 TFLOPS
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                756 TFLOPS
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                990 TFLOPS
            </td>
        </tr>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                FP32 Tensor
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                2 x 495 TFLOPS
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                378 TFLOPS
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                495 TFLOPS
            </td>
        </tr>
        <tr class="bg-gray-50">
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                FP64 Tensor
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                2 x 67 TFLOPS
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                51 TFLOPS
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                67 TFLOPS
            </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">
                NVLink 4 (600 GB/s)
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                NVLink 4 (600 GB/s)
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                NVLink 4, 18 Links (900 GB/s)
            </td>
        </tr>
        <tr class="bg-gray-50">
            <td class="border border-gray-300 px-4 py-2 font-semibold">GPU</td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                2 x GH100
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">GH100</td>
            <td class="border border-gray-300 px-4 py-2 text-center">GH100</td>
        </tr>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Transistor Count
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                2 x 80B
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">80B</td>
            <td class="border border-gray-300 px-4 py-2 text-center">80B</td>
        </tr>
        <tr class="bg-gray-50">
            <td class="border border-gray-300 px-4 py-2 font-semibold">TDP</td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                700-800W
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">350W</td>
            <td class="border border-gray-300 px-4 py-2 text-center">700W</td>
        </tr>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Interface
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                2 x PCIe 5.0 (Quad Slot)
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                PCIe 5.0 (Dual Slot)
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">SXM5</td>
        </tr>
    </tbody>
</table>

The choice between the NVIDIA H100 NVL and SXM5 ultimately depends on your specific project requirements. The NVL offers a more versatile and accessible option for various applications, while the SXM5 provides exceptional performance and scalability for demanding workloads.

If you prioritize flexibility and ease of integration, the NVL could be the better choice. However, if you need the highest possible performance and are willing to invest in a more specialized setup, the SXM5 is your ideal solution. Carefully consider your project's needs, budget, and infrastructure before making a decision.


A comparison of NVIDIA's high-end GPU options, the H100 NVL and H100 SXM5, for AI inference, AI training, and high-performance computing.

H100 NVL vs. SXM5: NVIDIA's Supercomputing GPUs


# August 2024 Update from Vast.ai

Welcome back to another monthly update from the team here at **Vast.ai**!

As the leading marketplace for affordable GPU rental, we're committed to not only maintaining our platform but also enhancing the ease of use and performance that our customers have come to appreciate.

To keep things running smoothly, we tackle bugs as quickly as possible. Below are some of the latest bug fixes we've made since our last update:

- **Billing page** now properly shows negative balance and failed payments.
- Added **Teams support for hosts**.
- **Cloud rsync** now creates destination drives instead of failing.
- **Instance-to-instance copy** now supports a progress bar and has better error handling.
- Fixed bug that prevented hosts from stopping their own instances.
- **Autoscaler** now delays stopping instances that are still working on tasks.
- Fixed issue that was causing **Autoscaler** to get stale search results.

## Cloud GPU Rental Platform Updates

At Vast.ai, we're always looking for ways to improve your experience. Beyond the usual maintenance, we've rolled out some updates this month that enhance our platform's performance and flexibility. Here are some highlights of the latest improvements:

- Launched a new **machine page** and **bulk edit feature**.
- **PCIe lanes** are now displayed on the machine view.
- Added **automatic soft blacklisting** to search based on a user's personal machine-reporting history.
- **Search backend** now supports continent options just like the front end.
- Added **GPU power/temperature limits** to offers and search (ALI/CPI only for now).
- Added a new batch version of stop/start instances to manage multiple instances within rate limits.
- Created a new **Blender template and guide**, as well as a new **Axolotl fine-tuning template**.
- Added a **recommended template for Stable Diffusion Fooocus**.

…And much more! Check out the full August 2024 change log below.

If you have any questions or need assistance with any aspect of our platform, we’re here to help! Whether you’re a new user or a seasoned pro, feel free to reach out through the chatbox at the bottom right, email us at **support@vast.ai**, or join our active **Discord community** for support and resources. We’d be delighted to assist you in getting the most out of Vast!

## Change Log

### Features:

- New **machine page** and **bulk edit feature**.
- Added **automatic soft blacklisting** to search based on user's personal report machine history.
- **Search backend** now supports continent options like the front end.
- **CLI/API logs command** now supports a grep option.
- Added **GPU power/temp limits** to offers & search (ALI/CPI only for now).
- Added **CLI/API option** to fetch system logs (useful for debugging custom image issues, copy/sync commands, etc.).
- Improved handling of failed **Docker commands** with retry and feedback to webserver to reduce zombie instances blocking GPUs.
- Added a new batch version of **stop/start instances** to manage multiple instances at once within rate limits.
- Added **Daemon logs** as an option to view on instance cards (next to existing instance logs).
- Added a loading spinner for **recommended templates** as the web UI starts.
- Added **recommended template for Stable Diffusion Fooocus**.
- Added optimization for **rsync & rcopy** when copying many small files.
- **PCIe lanes** displayed on machine view.

### Bug Fixes:

- Warning when copying a public template.
- Transfers now show properly on the **billing page**.
- **Autobilling threshold** fixed for team accounts.
- **GPU occupancy** now shows for unlisted machines.
- **GPU option search query filter** works.
- **Open button** for auth instances.
- **Reserved pricing** coherent display.
- **Billing page** properly shows negative balance and failed payments.
- **Cloud rsync** now creates destination drives instead of failing.
- Fixed env parsing bug in `create_instance` using `template_hash`.
- Added **Autoscaler tag** option for templates.
- Miscellaneous **UX/UI improvements**.
- Added **Teams support** for hosts.
- **Instance-to-instance copy** now supports progress bar, better error handling.
- **Autoscaler** now delays stopping instances that are still working on tasks.
- Fixed issue that was causing **Autoscaler** to get stale search results.
- **Offline display & machine list toggle**.
- **Bw_nvlink** added to machines.
- **Cloud Connections** can now be deleted properly on Cloud Page.
- Fixed **Kohya recommended template**.
- Fixed bug preventing hosts from stopping their own instances.
- Fixed inconsistent core counts for certain AMD Epyc CPUs.
- **Daemon speed test fix**: servers that timeout are now discarded.
- Fixed issue where **cloud copy commands** sometimes used incorrect paths.

### Miscellaneous Tasks:

- Added billing reminders.
- Removed obsolete error messages.
- Added **Llama3 TGI example & guide**.
- Created new **Blender template and guide**.
- Created new **Axolotl fine-tuning template**.
- Updated most of our recommended templates to use **TLS & auth** for open ports.
- Updated versions, tags, and readmes for recommended templates.

---

Stay tuned for more updates from Vast.ai!


Welcome back to another monthly update from Vast.ai! We've made several performance improvements, fixed bugs, and introduced new features to enhance your experience on our platform.

Posts about: Gpu Rental Platform

Why Choose Vast.ai to Train Custom AI Models

New Guide: Deploy MiniMax-M2 on Vast.ai

Which NVIDIA RTX 6000 Is Right for You?

H100 NVL vs. SXM5: NVIDIA's Supercomputing GPUs

August 2024 Product Update from Vast.ai

Posts about: Gpu Rental Platform

Why Choose Vast.ai to Train Custom AI Models

New Guide: Deploy MiniMax-M2 on Vast.ai

Which NVIDIA RTX 6000 Is Right for You?

H100 NVL vs. SXM5: NVIDIA's Supercomputing GPUs

August 2024 Product Update from Vast.ai

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