
If you're working at the cutting edge of AI and high-performance computing (HPC), NVIDIA's H200 and B200 GPUs are likely already very much on your radar. They're each designed to handle the massive compute demands of datacenter-class workloads – such as training trillion-parameter models and running high-speed inference at scale.

While both GPUs deliver incredible power, it's crucial to understand the differences between the two in order to make the right choice for your needs.

Today, we'll explore how the **H200** and **B200** measure up against each other and how each fits into real-world deployments. Which one is right for you? Let's take a look.

### NVIDIA H200: Hopper-Based Heavyweight

Building on the foundation laid by the H100, the NVIDIA H200 is based on the same Hopper architecture while bringing substantial improvements in memory capacity and bandwidth. This means fewer bottlenecks, an advantage that becomes increasingly important as you move further into enterprise scale.

The H200 nearly doubles the memory of its predecessor, boasting **141 GB of HBM3e memory** and **4.8 TB/s of bandwidth** – delivering about **1.4X faster** data access. MLPerf benchmarks using Llama 2 70B demonstrated the H200's power: it reached over 31,000 tokens per second, about **45% faster** than the H100.

With the same **fourth-gen NVLink**, **PCIe Gen5**, **MIG support**, and full Tensor Core and Transformer Engine capabilities, the H200 is a solid next step for existing Hopper-based deployments. You don't have to rethink your entire infrastructure; you just get more horsepower.

To sum up, here are some of the most notable features of the H200:

-   **141 GB of HBM3e memory at 4.8 TB/s** – almost double the memory of the H100 with 1.4X the bandwidth.

-   **Up to 1.9X improved LLM inference performance**, with more tokens per second and higher throughput for gen AI workloads compared to its predecessor.

-   **Lower energy use and total cost of ownership** (TCO) over the H100 thanks to improved energy efficiency and thermal management.

-   **An upgrade path exists on qualified HGX/MGX platforms**, so that little to no reconfiguration is required.

In short, the H200 represents the very best of the Hopper-based lineup. When it was introduced in late 2024, it set a new standard for memory-intensive and high-throughput workloads in datacenter GPUs – and it's still one of the most powerful GPUs on the market today.

The B200, on the other hand, is an entire generational leap forward.

### NVIDIA B200: The New Generation

Built on NVIDIA's advanced Blackwell architecture, the B200 features **192 GB of ultra-fast HBM3e memory** with **8.0 TB/s of memory bandwidth.** Those are some staggering numbers, even compared to the H200's respectable 141 GB at 4.8 TB/s.

The Blackwell architecture also introduces **fifth-gen Tensor Cores** and **dual Transformer Engines**, which are particularly useful for AI workloads with long context windows and heavy token parallelism. The B200 scales much more efficiently than the H200 for the latest and most advanced model classes.

In fact, scaling is a major area where the B200 stands out. With **fifth-gen** **NVLink**, its inter-GPU bandwidth reaches **1.8 TB/s** within a node – compared to Hopper's 900 GB/s – with cross-node scaling supported via the NVLink Switch System.

Of course, this may not matter to you very much if you're only using a single card. At cluster scale, however, or in multi-GPU systems like the DGX B200 (which connects eight B200 GPUs together), the improvements in distributed training efficiency really make a difference.

The B200 truly sits at the frontier of performance. Its raw capacity and speed can support next-gen inference pipelines and AI workloads at hyperscale. According to NVIDIA, it delivers **3X the training performance** and **15X the inference performance** of the previous generation.

These gains do come at a cost in power draw. The B200 has a max thermal design power (TDP) of **1000 W** compared to the H200 at 700 W – which means it also requires far more robust cooling solutions. But the extra power consumption may just be worth it for the boost in performance.

To recap, a few advantages of the B200 include:

-   **192 GB of HBM3e memory at 8.0 TB/s** – ultra-high capacity that's well suited for trillion-parameter models and allows training of AI models _entirely in GPU memory_.

-   **Fifth-gen Tensor Cores and second-gen Transformer Engines** for higher efficiency in FP8, FP16, and mixed precision performance, especially in long-context and multi-modal workloads.

-   **NVLink 5 interconnect** with up to **1.8 TB/s** GPU-to-GPU bandwidth for multi-node scaling.

-   **Fine-grain micro-tensor scaling** that optimizes performance and accuracy, enabling 4-bit floating point (**FP4**) AI.

The B200 was built to shatter performance records. As you can imagine, it has an eyewatering price tag to match. Nonetheless, it can be helpful to compare the B200 and H200 based on specs alone.

### H200 vs. B200: Key Specs Compared

Here's a side-by-side look at some key features of the H200 and B200:

<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-left font-semibold">
                H200
            </th>
            <th class="border border-gray-300 px-4 py-2 text-left font-semibold">
                B200
            </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">Hopper</td>
            <td class="border border-gray-300 px-4 py-2">Blackwell</td>
        </tr>
        <tr class="bg-gray-50">
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Memory
            </td>
            <td class="border border-gray-300 px-4 py-2">141 GB HBM3e</td>
            <td class="border border-gray-300 px-4 py-2">192 GB HBM3e</td>
        </tr>
        <tr>
            <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">4.8 TB/s</td>
            <td class="border border-gray-300 px-4 py-2">8 TB/s</td>
        </tr>
        <tr class="bg-gray-50">
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                CUDA Cores
            </td>
            <td class="border border-gray-300 px-4 py-2">16,896</td>
            <td class="border border-gray-300 px-4 py-2">16,896 x2</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">528 (4th Gen)</td>
            <td class="border border-gray-300 px-4 py-2">528 x2 (5th Gen)</td>
        </tr>
        <tr class="bg-gray-50">
            <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">1.98 GHz</td>
            <td class="border border-gray-300 px-4 py-2">1.98 GHz</td>
        </tr>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Transformer Engine
            </td>
            <td class="border border-gray-300 px-4 py-2">Single</td>
            <td class="border border-gray-300 px-4 py-2">Single (2nd gen)</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">SXM</td>
            <td class="border border-gray-300 px-4 py-2">SXM</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">
                NVLink 4 (900 GB/s) per GPU
            </td>
            <td class="border border-gray-300 px-4 py-2">
                NVLink 5 (1.8 TB/s) per GPU
            </td>
        </tr>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Precision Formats
            </td>
            <td class="border border-gray-300 px-4 py-2">
                FP8, FP16/BF16, TF32, FP32, FP64, INT8
            </td>
            <td class="border border-gray-300 px-4 py-2">
                FP4, FP6, FP8, FP16/BF16, TF32, FP32, FP64, INT8
            </td>
        </tr>
        <tr class="bg-gray-50">
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Multi-Instance GPUs
            </td>
            <td class="border border-gray-300 px-4 py-2">Up to 7</td>
            <td class="border border-gray-300 px-4 py-2">Up to 7</td>
        </tr>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Max Thermal Design Power (TDP)
            </td>
            <td class="border border-gray-300 px-4 py-2">700 W</td>
            <td class="border border-gray-300 px-4 py-2">1000 W</td>
        </tr>
        <tr class="bg-gray-50">
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Recommended Power Supply
            </td>
            <td class="border border-gray-300 px-4 py-2">1100 W</td>
            <td class="border border-gray-300 px-4 py-2">1400 W</td>
        </tr>
    </tbody>
</table>

Choosing the right GPU often comes down to more than just specs, however.

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

While both the H200 and B200 deliver game-changing performance capabilities, they each have distinct advantages depending on your needs.

#### You should consider the H200 if you prefer:

-   **A high-performance GPU** ideal for fine-tuning LLMs and high-throughput inference at the enterprise level without getting into exascale territory.

-   **A proven workhorse** that balances strong performance with lower TDP and cost.

-   **Being able to seamlessly upgrade** from existing Hopper deployments without overhauling infrastructure.

#### Or the B200 might be the right choice if you need:

-   **To push model boundaries** and handle long-context and/or multi-modal foundation model training at scale.

-   **The absolute maximum throughput and efficiency** for multi-GPU or multi-node clusters.

-   **A future-proof investment** for infrastructure built to support the next generation of cutting-edge AI and AGI.

### Conclusion

At **Vast.ai**, we know that the choice between GPUs like the H200 and B200 can depend on a lot more than just performance. Access and cost are important factors, as well.

The H200 and B200 represent the very best of NVIDIA's current lineup, and they're among the most powerful and expensive GPUs in the world today. Buying them outright is undoubtedly out of reach for most teams.

That's what we're here for. With Vast, you can rent high-end GPUs like the [A100](https://vast.ai/article/H100-vs-A100-Comparing-two-Powerhouse-GPUs), [H100](https://vast.ai/article/nvidia-h100-vs-l40s-power-meets-versatility), [H200](https://vast.ai/article/nvidia-h100-vs-h200-two-hopper-based-heavyweights), and soon the B200, with on-demand pricing and spot instances – and pay only for what you use.

On average, **Vast users save 5–6X on GPU compute** compared to traditional cloud providers. You'll find a variety of options on our platform to fit your needs, so you can focus on what really matters: your work, not your budget.

Explore Vast today and enjoy enterprise-grade compute without enterprise-level costs.


The H200 and B200 GPUs represent the top end of NVIDIA's current lineup for AI and high-performance computing (HPC). Both are designed for serious, large-scale workloads – from training foundation models to running complex scientific simulations.

NVIDIA H200 vs. B200: Comparing Datacenter-Grade Accelerators


The H100 and H200 GPUs represent the top end of NVIDIA's current lineup for AI and high-performance computing (HPC). Both are designed for serious, large-scale workloads – from training foundation models to running complex scientific simulations.

At a glance, the two GPUs may appear similar, but for those working across enterprise environments or pushing the boundaries of exascale computing, the differences between them can have a major impact on performance and scalability.

Today, we're taking a look at how the H100 and H200 stack up against each other to help you decide which of these two heavyweights may be the better fit for your needs.

### NVIDIA H100: The Proven Performer

The H100 GPU made waves when it launched in March 2023\. Quickly adopted by big names like OpenAI, Meta, and Stability AI, it became the engine behind some of the most advanced generative AI systems in production today.

Built on NVIDIA's innovative Hopper architecture, the H100 was engineered for scale, handling everything from trillion-parameter language models to high-throughput inference. It introduced **fourth-gen Tensor Cores** and a dedicated **Transformer Engine**, designed to accelerate deep learning workloads with speed and FP8 precision.

The H100 boasts up to a **30X boost in performance** for large language models (LLMs) over its predecessor, the A100 GPU, according to NVIDIA. This massive improvement helped cement the H100 as the new standard for foundational model training, and to this day, it's still one of the very best GPUs on the market.

#### Some of the H100's more notable features include:

-   **80 GB of HBM3 memory**, with some models offering up to 94 GB.

-   **3.35 TB/s of memory bandwidth** to handle data-intensive workloads.

-   **Fourth-gen Tensor Cores** with superior throughput performance, along with an advanced Transformer Engine and FP64 and FP8 capabilities to reduce memory usage.

-   **PCIe Gen5 and fourth-gen NVLink** (with 900 GB/s of GPU-to-GPU interconnect), as well as NDR Quantum-2 InfiniBand networking for efficient scalability.

-   **Multi-Instance GPU (MIG) support** for secure partitioning into up to 7 isolated right-size instances that maximize quality of service for multi-tenant environments and parallel workloads.

If your projects involve massive datasets, large-scale simulations, or advanced AI training for foundational models, the H100 has proven itself as a reliable, high-performance solution. But with the arrival of the H200 in late 2024, NVIDIA has taken things a step further, introducing upgrades that push the envelope even more for memory-intensive and high-throughput workloads.

We ran a side-by-side comparison of inference performance on the **DeepSeek R1 Distill Llama 8B** model using `vLLM` with both NVIDIA H100 and H200 GPUs on Vast.ai.

#### **Benchmark Configuration**

-   **Model:** `deepseek-ai/DeepSeek-R1-Distill-Llama-8B`
-   **Requests:** 5,000
-   **Batch:** 512
-   **Input Tokens:** 16
-   **Output Tokens:** 128
-   **Backend:** `vLLM`

---

### **Results**

<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">
                Metric
            </th>
            <th class="border border-gray-300 px-4 py-2 text-center font-semibold">
                H100
            </th>
            <th class="border border-gray-300 px-4 py-2 text-center font-semibold">
                H200
            </th>
        </tr>
    </thead>
    <tbody>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Benchmark Duration
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                45.28 s
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                40.02 s
            </td>
        </tr>
        <tr class="bg-gray-50">
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Request Throughput
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                110.42 req/s
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                124.93 req/s
            </td>
        </tr>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Output Throughput
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                13,650 tok/s
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                15,435 tok/s
            </td>
        </tr>
        <tr class="bg-gray-50">
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Total Throughput
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                15,416 tok/s
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                17,434 tok/s
            </td>
        </tr>
    </tbody>
</table>

### NVIDIA H200: The Supercharged Successor

The H200 builds directly on the foundation laid by the H100, bringing meaningful improvements that target one of the biggest bottlenecks in AI and HPC workloads: memory.

While it shares the same Hopper architecture as the H100 and has similar core compute specs, the H200 significantly increases both memory capacity and bandwidth, two factors that become critical when operating at enterprise scale or pushing into exascale territory.

Equipped with **141 GB of HBM3e memory** and **4.8 TB/s of bandwidth**, the H200 nearly doubles the memory of the H100 and delivers about **1.4X faster** data access. Early benchmarks reflect that improvement. In MLPerf testing using Llama 2 70B, the H200 achieved over 31,000 tokens per second – about **45% faster than the H100**.

<img
    src="/uploads/h100vsh200_2.png"
    alt="Image credit: NVIDIA"
    style={{ width: "100%" }}
/>

The H200 retains all of the architectural strengths of the H100, including **fourth-gen NVLink**, **PCIe Gen5**, **MIG support**, and full **Tensor Core and Transformer Engine capabilities**. But for developers and organizations looking to squeeze out more performance without overhauling their infrastructure, the H200 offers a straightforward upgrade path with [measurable gains](https://resources.nvidia.com/en-us-data-center-overview/hpc-datasheet-sc23-h200).

#### To recap, here are a few advantages of the H200:

-   **141 GB of HBM3e memory at 4.8 TB/s** – nearly double the capacity of the H100, with 1.4X more memory bandwidth.

-   **Up to 1.9X the inference performance** of the H100 when handling LLMs, with more efficient processing per token and per inference cycle.

-   **50% reduced energy use and total cost of ownership** (TCO) due to improved thermal management and energy efficiency.

-   **Drop-in compatibility with existing Hopper-based systems**, enabling accelerated performance gains in H100-optimized infrastructure with minimal or no additional changes.

In short, the H200 represents a meaningful leap beyond the H100 in memory capacity, bandwidth, and throughput, bringing a new level of performance to AI and HPC workloads. That comes at a premium price, of course. But if you're weighing the tradeoffs between the two GPUs in terms of specs alone, let's take a side-by-side look at how they compare.

### H100 vs. H200: Key Specs Compared

For reference, here's a quick outline of the various features and specs of the NVIDIA H100 and H200:

<table class="w-full border-collapse border border-gray-300">
    <thead>
        <tr class="bg-gray-100">
            <th class="border border-gray-300 px-4 py-2 text-left font-semibold">
                Specification
            </th>
            <th class="border border-gray-300 px-4 py-2 text-center font-semibold">
                H100
            </th>
            <th class="border border-gray-300 px-4 py-2 text-center font-semibold">
                H200
            </th>
        </tr>
    </thead>
    <tbody>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                GPU Architecture
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">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">
                GPU Memory
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                80 GB HBM3
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                141 GB HBM3e
            </td>
        </tr>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                GPU Memory Bandwidth
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                3.35 TB/s
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                4.8 TB/s
            </td>
        </tr>
        <tr class="bg-gray-50">
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                FP64 Tensor Core
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                33.5 TFLOPS
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                33.5 TFLOPS
            </td>
        </tr>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">FP32</td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                67 TFLOPS
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                67 TFLOPS
            </td>
        </tr>
        <tr class="bg-gray-50">
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                TF32 Tensor Core
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                989 TFLOPS
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                989 TFLOPS
            </td>
        </tr>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                FP16 Tensor Core
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                1,979 TFLOPS
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                1,979 TFLOPS
            </td>
        </tr>
        <tr class="bg-gray-50">
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                FP8 Tensor Core
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                3,958 TFLOPS
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                3,958 TFLOPS
            </td>
        </tr>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                INT8 Tensor Core
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                3,958 TOPS
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                3,958 TOPS
            </td>
        </tr>
        <tr class="bg-gray-50">
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Decoders
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                7 NVDEC, 7 NVJPEG
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                7 NVDEC, 7 NVJPEG
            </td>
        </tr>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                L2 Cache
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">50 MB</td>
            <td class="border border-gray-300 px-4 py-2 text-center">50 MB</td>
        </tr>
        <tr class="bg-gray-50">
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Max. Thermal Design Power
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                Up to 700 W
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                Up to 1000 W
            </td>
        </tr>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Multi-Instance GPUs
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                Up to 7 MIGs @ 10GB each
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                Up to 7 MIGs @ 18GB each
            </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">SXM</td>
            <td class="border border-gray-300 px-4 py-2 text-center">SXM</td>
        </tr>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Interconnect
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                PCIe 5.0 x16
            </td>
            <td class="border border-gray-300 px-4 py-2 text-center">
                PCIe 5.0 x16
            </td>
        </tr>
    </tbody>
</table>

Ultimately, choosing the right GPU largely depends on your workload demands and performance priorities.

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

While both the H100 and H200 excel in AI and HPC environments, they serve slightly different needs.

#### You should consider the H100 if you prefer:

-   **A proven, high-performance GPU** for training medium to large models and handling intensive AI or HPC workloads.
-   **Use cases involving compute-bound workloads**, where total memory capacity is not as critical.
-   **A time-tested solution** already in widespread production use, balancing performance with lower power and cost.

#### Whereas the H200 may be a better fit if you need:

-   **Greater memory capacity and bandwidth** for running _even larger_ models, handling memory-bound workloads, or scaling LLM inference on the enterprise level.
-   **Higher throughput**, especially for tasks involving extended context windows, multi-modal AI, or very large batch sizes.
-   **A forward-looking upgrade path** with improved energy efficiency and lower TCO, thanks to higher performance per watt.

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At **Vast.ai**, we understand that choosing a GPU isn't only about specs. Often, the decision comes down to access and affordability, as well – especially when we're talking about some of the most powerful and expensive GPUs in existence. (Speaking of, check out our previous posts comparing the [H100 vs. A100](https://vast.ai/article/H100-vs-A100-Comparing-two-Powerhouse-GPUs), and the [H100 vs. L40S](https://vast.ai/article/nvidia-h100-vs-l40s-power-meets-versatility).)

That's why our cloud GPU rental platform is built to offer high-performance compute at a fraction of the typical cost. Vast gives you flexible, cost-effective access to H100s, H200s, and more.

With options for on-demand pricing and budget-friendly spot instances, you can scale up as needed without overspending. On average, **Vast users save 5- 6X on GPU compute** compared to traditional cloud providers.

Explore our platform today and get the power you need, when you need it – with no upfront investment required.


The H100 and H200 GPUs represent the top end of NVIDIA's current lineup for AI and high-performance computing (HPC). Both are designed for serious, large-scale workloads – from training foundation models to running complex scientific simulations.

Posts about: H200

NVIDIA H200 vs. B200: Comparing Datacenter-Grade Accelerators

NVIDIA H100 vs. H200: Two Hopper-based Heavyweights