NVIDIA H100 vs A30 – Performance, Features, and Pricing Compared

Are you trying to decide between the NVIDIA H100 and A30 GPUs for your AI, ML, or data center workloads? Both are powerful accelerators built by NVIDIA, but they cater to very different segments of users. While the A30 focuses on energy efficiency and enterprise-grade inference performance, the H100 is an absolute powerhouse built for next-generation AI training, generative workloads, and deep learning at scale.

In this blog, we’ll break down the differences between NVIDIA H100 and A30 GPUs, compare their performance benchmarks, pricing, and ideal use cases. By the end, you’ll know which GPU fits your workload, budget, and long-term computing needs.

Overview: The Evolution from A30 to H100

NVIDIA’s A30 belongs to the Ampere architecture generation, while the H100 is part of the newer Hopper architecture. Each generation brings massive advancements in processing cores, memory speed, and AI-optimized performance.

The A30 excels in inference, virtualization, and data analytics — perfect for enterprises that prioritize efficiency and scalability. On the other hand, the H100 is engineered for AI model training, large-scale simulations, and generative AI — designed for organizations that need maximum compute power.

Technical Comparison: H100 vs A30

The specs alone show how different these GPUs are in purpose and scale. The H100 dramatically increases performance in AI workloads while consuming more power and demanding a larger infrastructure setup.

Performance Benchmark: AI and Machine Learning Workloads

Let’s look at how both GPUs perform across different categories of workloads:

1. AI Model Training

H100: Trains large models like GPT, Llama, and BERT at unprecedented speeds. Up to 5× faster than the A100 and nearly 20× faster than the A30.

A30: Ideal for smaller models and inference workloads but lacks the raw power needed for full-scale training.

2. AI Inference

H100: Supports real-time inferencing for large models but is overkill for lightweight deployments.

A30: Optimized for inference efficiency, delivering high throughput with lower power consumption.

3. Virtualization and Multi-Instance GPU (MIG)

Both GPUs support NVIDIA MIG technology, but the A30 is more suited for multi-tenant environments due to its lower power draw. The H100’s focus is performance density rather than efficiency.

4. HPC and Data Science

H100: Dominates in HPC and deep learning, supporting NVLink, HBM3 memory, and high parallelism for demanding research workloads.

A30: Handles analytics, moderate simulations, and enterprise workloads with ease but cannot match the H100’s throughput.

Pricing in India (2025 Update)

The price gap between these two models is significant — the H100 costs almost 6–8× more than the A30. However, that difference reflects a huge performance leap for advanced AI workloads.

Power Efficiency and Scalability

Power consumption and cooling are key factors in data center environments:

The A30 consumes just 165W, making it ideal for energy-conscious organizations that want scalable GPU clusters with lower operational costs.

The H100, on the other hand, draws up to 700W, requiring robust cooling and specialized infrastructure. It’s optimized for maximum throughput rather than energy efficiency.

If you’re running hundreds of inference tasks daily, the A30 is the better option. But if you’re training massive AI models, the H100 is worth every rupee.

Use Case Breakdown: Choosing Between A30 and H100

Return on Investment (ROI) Considerations

When choosing between the A30 and H100, think about total cost of ownership (TCO) and expected performance gains.

The A30 offers a fast ROI for organizations focused on steady AI inference workloads or smaller AI projects.

The H100 provides massive long-term value for enterprises in generative AI, scientific research, and complex machine learning ecosystems.

Cloud GPU Option: The Smarter Alternative

If your organization wants access to H100 or A30 power without investing lakhs in hardware, Cyfuture Cloud offers on-demand GPU hosting in India.

With GPU cloud instances, you can choose between A30, A100, and H100 GPUs — all billed transparently by usage. Whether you need power for a day or a month, cloud GPU rentals deliver flexibility and scalability at a fraction of the cost.

Benefits of Choosing Cyfuture Cloud

On-demand access to NVIDIA A30 and H100 GPUs

Indian data centers for low latency and data security

Transparent hourly or monthly pricing

Scalable infrastructure for AI, ML, and HPC workloads

24/7 support and enterprise-grade reliability

This approach allows startups, developers, and research teams to harness world-class GPUs without high capital investment.

Conclusion

The NVIDIA A30 and H100 represent two ends of NVIDIA’s data center GPU lineup — one prioritizing efficiency, the other raw power.

If your goal is AI inference, virtualization, or scalable cloud deployments, the A30 remains the best balance of performance and cost. But if you’re diving into AI model training, generative AI, or high-performance computing, the H100 is the ultimate investment.

For organizations that want flexibility, Cyfuture Cloud’s GPU as a Service provides both A30 and H100 options, delivering enterprise-grade performance with affordable and transparent pricing — so you can innovate faster without compromising on efficiency or budget.