Artificial Intelligence has become an integral part of nearly all industries in the form of chatbots to driverless cars. It is all based on high-performance hardware, which is capable of handling large volumes of data quickly. The core of that hardware is the GPUs, or graphics processing units.
NVIDIA is the leader in this space with its high-tech AI chips. H100 and H200 are two flagship models. They build and train modern AI systems. India has a large number of companies and researchers who demand these GPUs when setting up machine learning and deep learning projects.
This blog describes both GPUs in simple terms. It discusses what each of them provides, their differences, and the way the Indian hosting provider Cantech uses them to host AI. At the end, you will be able to understand which GPU is the most suitable for your needs and budget.
What is NVIDIA H100?
The H100 is a graphics card that is a part of the Hopper architecture of NVIDIA. It was released in 2022 as the first chip to utilize the new compute engine created by Hopper to train very large AI models. The H100 also introduced numerous hardware capabilities to accelerate training and inference.
It is compatible with HBM3 memory, which allows rapid data transfer between memory and the cores of the GPU. It also introduced the Transformer Engine technology that enables deep-learning models based on transformer layers to run more effectively. H100 also supports SXM and PCIe boards, which data centers can select to meet their cooling and power requirements.
The primary objective of the H100 is to handle AI workloads that are very high throughput. These are big language models, recommendation systems, and research simulations.
Having known the H100 and its position in the NVIDIA product line, we can proceed to the next generation. The H200 is based on the H100 and has some essential improvements.
What is NVIDIA H200?
The H200 GPU arrived in late 2024. It is also based on the same Hopper family, and NVIDIA made it to eliminate some of the restrictions that users were facing with the H100. The largest modification is the upgrade of HBM3 to HBM3e memory. This type of memory has a higher bandwidth and capacity, which means that large-scale AI models can be run much faster and fit in entirety into the memory of a GPU.
Energy use and efficiency are also enhanced in the H200. The H200 has higher computations per watt, although its overall power rating remains approximately the same as the H100. It can also be used with the NVLink interconnect technology of NVIDIA, allowing multiple GPUs to be used within a single server node.
This GPU is for data centers that train or fine-tune very large models like GPT-4, Gemini, or LLaMA-3. Its increased memory capacity minimizes the necessity to divide datasets or models into multiple GPUs, which saves time and makes the model less complex.
H100 vs H200: Major Technical differences
The following table indicates factual differences between the two GPUs.
| Specification | H100 | H200 |
| Architecture | Hopper | Enhanced Hopper |
| Memory Type | HBM3 | HBM3e |
| Memory Capacity | 80 – 94 GB | 141 GB |
| Memory Bandwidth | 3.9 TB/s | 4.8 TB/s |
| Compute Cores (FP32 Tensor) | 989 TFLOPS | 989 TFLOPS (same core count) |
| FP8 Tensor Core | 3,958 TFLOPS | 3958 TFLOPS |
| NVLink Bandwidth | 900 GB/s | 900 GB/s |
| Multi-Instance GPU (MIG) | Up to 7 partitions of ~10/12 GB each | Up to 7 partitions of ~18.5 GB each |
| Typical Power Use (SXM) | 700 W | 700 W |
| Launch Year | 2022 | 2024 |
H100 vs H200: Performance and Real-Life Usage Comparison
Memory and Bandwidth
AI models keep growing. The increase in memory capacity to 141GB from 80GB allows the H200 to accommodate significantly larger models on one card. Increased bandwidth speeds up data transfer, reducing the time per training iteration. This advantage is particularly evident in the case of long-context language models or large image models.
Training Speed
In real tests by several hosting companies, the H200 is capable of training transformer-based models at a speed of 1.5 times the H100 under identical conditions. The enhancement is primarily due to accelerated memory movement as opposed to raw core count.
Efficiency
Both GPUs consume the same amount of power, whereas the H200 finishes more work per watt. This is important to data centers since it reduces the total energy bill. That is why the H200 is more appropriate in the long-running AI projects where efficiency is important.
Cantech’s Offerings with H100 and H200
Cantech offers NVIDIA H100 and H200 GPUs in servers, among other basic to advanced pricing plans. Our infrastructure can support machine learning, deep learning, simulation, data analytics, etc. Every server has enterprise cooling, high-speed networking, and secure access controls.
Cantech boasts of 24/7 availability and backup power, and a route. Our platform does GPU monitoring, driver maintenance, and environment configuration of frameworks such as PyTorch and TensorFlow. This saves time that would be taken by the user in system administration.
Infrastructure is optimized towards high-memory-bandwidth workloads and training tasks with long sequence lengths.
The H200 systems have the same cooling and power backbone as the H100 systems. This implies that upgrades can be done with low modification of the current rack layout. The H200 access can be requested by the customers via multiple support channels to get H200 access.
In both GPUs, Cantech offers 24/7 technical support. We keep servers in a controlled environment and can perform correct thermal performance. We also have flexible billing schemes that can accommodate startups, research organisations, or companies testing large-scale AI systems.
Now that we know the way Cantech employs these GPUs, it is time to make a decision on which one will be more appropriate for your workload or the type of project you are working on.
H100 vs H200: Which GPU is the best?
The decision between the H100 and H200 is based on your model size, memory requirement, and budget. One of the strongest GPUs in the market, the H100 can easily cope with medium-to-large training and is cheaper than the H200.
The H200 can be used with very large datasets or models that require over 80GB of memory. Its additional 61GB and increased bandwidth imply that you can store models in one GPU, which saves time and makes scripts easier.
The H200 is more efficient as it performs more work per unit of power, and this is useful when using large clusters that will run for months. The H100 offers a more cost-effective performance when it comes to short-term training or mixed workloads.
Conclusion
The NVIDIA H100 and H200 are both leaders of the field of GPU computing, each of them being applicable to a different level of AI development.
The H100 will last you many years in case your workloads are consistent, and your models fit in 80GB. It is stable, and all major ML frameworks are supported and tested.
The H200 is the smarter option in case you require huge context windows, lengthy training cycles, or extremely large models. It is more efficient since its larger memory and faster HBM3e bandwidth become more effective as datasets increase.
The GPU hosting in India provided by Cantech is available in both GPUs, where one can use the H100 and upgrade to the H200 later with only slight configuration changes. This scalability assists companies in expanding AI capacity at a slow and affordable pace.
In other words, H100 is an established performer, whereas H200 is the future of high-memory AI workloads. It will be determined by what you need at the moment and what you want to do in the future.
FAQs
Are the H100 and H200 GPUs capable of training large language models?
Yes. The H100 and H200 are capable of training large language models, and the H200 can train even larger ones due to its additional memory and bandwidth.
Does the H200 consume more power?
No. Both GPUs are rated equally in terms of power – approximately 700 W in SXM models. The H200 is more efficient in output per watt.
Do H100 and H200 GPUs support inference and training?
Yes. Inference is the strong point of both GPUs, and the H200 is smoother when dealing with large contexts or batched requests.
Is the H200 backward compatible with existing servers?
Yes, in most cases. The H200 is based on the Hopper architecture and NVLink interface; thus, most H100-compatible systems can be modified into H200 with slight modifications.
Is it time to upgrade to H200?
Not always. Unless your existing projects exceed the capacity of the H100, then there is no immediate necessity to change. Upgrading is only reasonable when your data or model size grows significantly.
