GPU
An overview of the NVIDIA H200 GPU
Inside the NVIDIA H200: Specifications, use cases, performance benchmarks, and a comparison of H200 vs H100 GPUs.
Accelerate your AI with NVIDIA H200 GPUs on Ori to train models and run inference more efficiently than ever before.
We’re thrilled to announce the general availability of NVIDIA’s cutting-edge H200 Tensor Core GPUs across our global platform. The NVIDIA H200 GPU supercharges AI workloads with game-changing performance and memory capabilities, and now you can leverage this powerhouse on Ori’s end-to-end AI cloud.
Realize a new level of performance for your AI
Built on the advanced Hopper architecture, the H200 delivers leaps in throughput for both AI and HPC workloads. It boosts AI inference speed by up to 2x versus the H100 on large language models, enabling faster responses and higher user capacity. This means quicker training runs, snappier AI inference, and accelerated time-to-market.
Larger and faster memory for higher efficiency
An important hurdle to advancing AI progress is the memory wall. Model attributes such as accuracy, sequence length and latency are either directly or indirectly influenced by memory bandwidth and memory capacity of GPUs. Ample and fast memory is an essential requirement to realize the full computational benefits of a high performance GPU architecture such as Hopper.
Each H200 GPU comes equipped with 141 GB of HBM3e memory running at 4.8 TB/s. That’s 76% more memory capacity and 43% higher memory bandwidth than the H100 GPU. This enormous, fast memory pool allows ML developers to fit larger models and datasets into a single GPU, reducing the need for model sharding. It also improves latency for inference, letting models fully exploit Hopper’s compute advances. In practical terms, many models that wouldn’t fit in a single H100 GPU can now run on an H200 GPU, helping ML teams build AI more efficiently.
What can you do with the NVIDIA H200 GPU?
Train & finetune large models: The faster and expanded NVIDIA H200 memory enables improved training and inference for state-of-the-art (SOTA) models. Whether you are building foundation models or training compute-intensive models such as image and video generation, H200 GPUs are a great choice for models that are trained on vast amounts of data.
Run inference on 100+ billion parameter models with ease: The enhanced HBM3E memory capabilities of the H200 GPU makes it easier to run inference with much longer input and output sequences with tens of thousands of tokens.
Models with large parameter count now need lesser GPUs. For example, DeepSeek R1 671B which needs about two nodes (consisting of 8 H100 GPUs each) for inference can run on a single H100 node. That means you can serve large models at scale with more efficiently with H200.
Power high-precision HPC workloads: Whether it is scientific models, simulations or research projects, increased memory capacity helps to run models with higher precision formats such as FP32 and FP64 for maximum accuracy, and higher memory bandwidth reduces computing bottlenecks.
Deploy Enterprise AI with greater efficiency: Enterprise AI apps typically run on large GPU clusters, the H200 GPU makes it easy to manage infrastructure with fewer GPUs, greater utilization and enhanced throughput for better ROI.
Why Choose Ori for Your H200 GPU Needs?
Ori Global Cloud isn’t just offering the latest NVIDIA hardware – we provide an entire ecosystem to maximize its value for you. Here are some compelling reasons developers and businesses choose Ori for GPU-intensive workloads:
- End-to-End Platform: Ori accelerates the entire AI pipeline. From running experiments with GPU Instances to training large models with GPU clusters, scaling workloads with Serverless Kubernetes, serving models with Inference Endpoints or building Enterprise AI with a Private Cloud, you can do it all on a single platform. No more switching environments or waiting for access, get your AI models and applications to market faster with Ori.
- Flexible, Cost-Efficient Usage: We offer flexible pricing models ranging from on-demand pricing to reserved instances and Private Cloud-as-a-Service. Ori provides you H200 GPUs at a fraction of the prices from hyperscale clouds with no surprise fees.
- Seamless Deployment: Ori is designed for ease of use and scalability as demand for your models and apps grows. You can spin up H200-powered virtual machines via our cloud console, API or CLI with all the necessary NVIDIA drivers and ML frameworks ready to go. If you prefer Kubernetes for your AI workloads, Our Serverless Kubernetes service allows you to scale automatically without the hassles of managing infrastructure.
- Reliability and Expert Support: When you run on Ori, you’re leveraging a platform engineered for demanding AI workloads. Our environments are monitored 24/7 and built on enterprise-grade hardware and networking. Ori’s team has deep expertise in AI infrastructure, we’ve worked on deployments of large language models and GPU clusters at scale.
Get Started with NVIDIA H200 on Ori
Ori Global Cloud provides flexible infrastructure for any team, model, and scale. Backed by top-tier GPUs such as the NVIDIA H200, performant storage, and AI-ready networking, Ori enables growing AI businesses and enterprises to deploy their AI models and applications in a variety of ways:
- Leverage GPU Instances as on-demand virtual machines.
- Deploy Private Clouds for flexible and secure enterprise AI.
- Operate Inference Endpoints effortlessly at any scale.
- Scale GPU Clusters for training and inference.
- Manage AI workloads on Serverless Kubernetes without infrastructure overhead.
Ready to accelerate your AI workloads with NVIDIA H200 GPUs?
