NVIDIA H200 GPU Tensor Core Card 141GB

The rapid evolution of artificial intelligence has pushed computing infrastructure to its limits. As large language models (LLMs), generative AI, and data-intensive workloads continue to expand, organizations need GPUs that deliver not only raw compute power but also massive memory bandwidth and efficiency. The NVIDIA H200 GPU represents a significant step forward in this direction.

Built on NVIDIA’s Hopper architecture, the H200 GPU is designed specifically for AI training, AI inference, large language models, and high-performance computing (HPC). With a major upgrade in memory capacity and bandwidth, it addresses one of the biggest bottlenecks in modern AI systems—data movement at scale.

At Cyfuture Cloud, the NVIDIA H200 GPU is available through flexible GPU as a Service (GPUaaS) models, enabling enterprises to deploy cutting-edge AI infrastructure without heavy upfront investment.

What Is the NVIDIA H200 GPU?

The NVIDIA H200 GPU is a high-performance Tensor Core GPU from the Hopper family, purpose-built for enterprise AI and data center workloads. Its most notable advancement is the introduction of 141 GB of HBM3e memory with up to 4.8 TB/s memory bandwidth, nearly doubling memory capacity compared to the H100.

This upgrade allows organizations to run:

• Larger AI models

• Higher batch sizes

• Faster inference pipelines

• More complex HPC simulations

In real-world scenarios, the H200 delivers up to 2× faster LLM inference compared to the H100 gpu, making it ideal for production-grade AI systems.

Technical Breakdown of the NVIDIA H200 GPU

The H200 builds on the strengths of Hopper architecture while addressing memory-bound AI workloads.

Core Specifications

• CUDA Cores: 16,896

• Tensor Cores: 528 (4th Generation)

• Memory: 141 GB HBM3e

• Memory Bandwidth: Up to 4.8 TB/s

• Tensor Performance: Up to 3,958 TFLOPS (FP8 / INT8)

• Form Factors: SXM and PCIe

• TDP: Up to 700W (SXM variant)

The enhanced Transformer Engine dynamically optimizes precision (FP8, FP16, FP32) for transformer-based models, improving both performance and efficiency.

Why Memory Matters for AI and LLMs

Modern AI workloads are increasingly memory-constrained, not compute-constrained. Large models with hundreds of billions—or even trillions—of parameters require fast access to vast datasets.

The H200’s HBM3e memory enables:

• Lower latency during inference

• Fewer memory swaps

• Faster training convergence

• Support for models up to 1.8 trillion parameters

This makes the H200 particularly effective for generative AI, multimodal models, and real-time AI inference.

NVIDIA H200 on Cyfuture Cloud (GPU as a Service)

Cyfuture Cloud integrates the NVIDIA H200 GPU into enterprise-grade GPU clusters, offering it through GPU as a Service (GPUaaS).

Key Advantages with Cyfuture Cloud

• On-demand H200 GPU access

• Multi-GPU clusters with NVLink (up to 900 GB/s)

• MIG support (up to 7 isolated GPU instances per card)

• Secure, India-based data centers

• 99.99% uptime SLA

• No CapEx—pay only for what you use

This approach allows startups, enterprises, and research teams to scale AI workloads without purchasing hardware costing $30,000–$46,000 per GPU.

Use Cases for NVIDIA H200 GPU

The H200 GPU on Cyfuture Cloud is optimized for:

• Large Language Models (LLMs) – training and inference

• Generative AI – text, image, video, and multimodal models

• AI Inference at Scale – low-latency production workloads

• High-Performance Computing (HPC) – simulations, modeling

• Enterprise AI Pipelines – fine-tuning, MLOps, deployment

NVIDIA H200 vs H100: What’s the Difference?

While compute performance remains similar, the key advantage of the H200 lies in memory.

For memory-intensive AI workloads, the H200 offers significantly better cost-per-token efficiency.

Conclusion

The NVIDIA H200 GPU is a critical advancement for organizations building next-generation AI systems. Its unmatched memory capacity, bandwidth, and AI-optimized architecture make it ideal for large-scale inference, training, and HPC workloads.

With Cyfuture Cloud’s GPU as a Service, businesses gain immediate access to H200-powered infrastructure—without long procurement cycles or capital expenditure. This combination delivers faster time-to-value, lower total cost of ownership, and production-ready AI performance.

Frequently Asked Questions (FAQs)

What is an H200 GPU?

The NVIDIA H200 GPU is a Hopper-based Tensor Core GPU built for large-scale AI, large language models (LLMs), and high-performance computing (HPC). It features 141 GB of HBM3e memory and up to 4.8 TB/s memory bandwidth, enabling faster AI training and inference for memory-intensive workloads.

How much does an H200 GPU cost?

A physical NVIDIA H200 GPU typically costs between $30,000 and $46,000, depending on configuration. With Cyfuture Cloud GPU as a Service (GPUaaS), businesses can access H200 GPUs on a flexible, pay-as-you-use model without large upfront investment.

Is the H200 good for gaming?

No. The H200 GPU is designed exclusively for enterprise AI, data centers, and HPC workloads. It is not optimized for gaming or consumer graphics applications.

What is the difference between NVIDIA H100 and H200?

The H200 offers nearly double the memory of the H100 (141 GB vs 80 GB) and significantly higher memory bandwidth (up to 4.8 TB/s vs ~3.35 TB/s). While compute performance is similar, the H200 delivers 1.9×–2× faster LLM inference due to reduced memory bottlenecks.

Can NVIDIA MX150 run GTA 5?

Yes, the NVIDIA MX150 can run GTA 5, but only at low graphics settings and lower resolutions.

Which is better: GTX or RTX?

RTX GPUs are better than GTX GPUs because they support ray tracing, DLSS, and newer architectures, offering higher performance and better efficiency.

Which is better: H200 or B200 GPU?

The B200 GPU is newer and more powerful, but the H200 remains highly effective and widely deployed for enterprise AI, LLM inference, and HPC workloads, especially where memory capacity is critical.

What are the top 5 best GPUs?

“Best” depends on the use case. Enterprise AI GPUs like the H200 differ significantly from gaming GPUs, which prioritize frame rates and visuals rather than AI throughput.

Can an RTX 3060 run 240 FPS?

An RTX 3060 can reach 240 FPS only in lightweight or esports titles at low or medium settings. It is not guaranteed for all modern games.

Which is the No. 1 graphics card?

For enterprise AI and data center in India workloads, GPUs like the NVIDIA H200 and newer architectures lead in performance and scalability.

Is A100 better than RTX 4090?

For AI and HPC workloads, yes. The A100 is designed for data centers, while the RTX 4090 is optimized for gaming and creative workloads.

Is RTX 30 or RTX 40 better?

The RTX 40 series offers better performance, power efficiency, and advanced features compared to the RTX 30 series.

Does Quadro 2000 support 4K?

No, the Quadro 2000 does not officially support 4K output.

What GPU has the highest FPS?

FPS depends on the game, resolution, and settings. Gaming GPUs vary widely, while enterprise GPUs like the H200 are not designed for FPS-based workloads.

Is 8GB or 16GB better for a GPU?

16GB is better, especially for modern applications, higher resolutions, and AI or data-intensive workloads.

How does the H200 compare to the H100 in real workloads?

The H200 matches the H100’s compute performance but doubles memory to 141 GB HBM3e and increases bandwidth to 4.8 TB/s, resulting in 1.9×–2× faster LLM inference and improved cost-per-token efficiency.

What workloads is the H200 best for on Cyfuture Cloud?

The H200 is ideal for generative AI training and inference, large language models (such as 70B+ parameter models), HPC simulations, and multimodal AI workloads. Cyfuture Cloud provides scalable H200 clusters for seamless deployment.

Is the H200 available now on Cyfuture Cloud?

Yes. The NVIDIA H200 GPU, released in mid-2024, is available via Cyfuture Cloud GPUaaS, offering flexible hourly and monthly pricing without hardware ownership.

Can I use MIG on H200 with Cyfuture Cloud?

Yes. The H200 supports Multi-Instance GPU (MIG) with up to 7 isolated GPU instances per card, enabling secure multi-tenancy for development, testing, and production environments on Cyfuture Cloud.