NVIDIA GPUs: Comparing A100 vs H100 for Optimizing AI Workloads

NVIDIA GPUs: Comparing A100 vs H100 for Optimizing AI Workloads

For teams building for AI systems and HPC capabilities or workloads, choosing the right GPU is the difference between training models in days or weeks. The right tools for the right project optimizes the experience on the backend, and for the user as well. That’s why for artificial intelligence it’s important to know when it makes sense to use NVIDIA's A100 versus its H100 GPUs.

Both are both designed for high-performance computing, but serve different needs depending on your workload, scale, and budget. This article breaks down the core performance differences, ideal use cases, and architectural features so you can choose the best option for your project. First, lets start with their cores.

CUDA vs Tensor Cores

Compute Unified Device Architecture (CUDA) cores are designed to run multiple tasks at the same time. This makes them good for data processing, simulations, rendering video and traditional machine learning. Think of them as an engine that can power whatever GPU-accelerated task it is plugged into.

Tensor cores are specialized for matrix operations in deep learning. They manipulate numbers more efficiently than CUDA cores, especially when used in mixed-precision formats like FP16, BF16, or INT8. Think of them as your copilot for deep learning and matrix math.

‍Bottom line: Tensors are better for matrix ops, CUDA is better for general-purpose compute.

When to Use A100 GPU vs. H100

NVIDIA A100

This GPU offers a better balance of performance and cost-efficiency for training and inference thanks to its tensor core gpu. This makes it better suited for:

• Working with a variety of AI models, including both training and inference tasks.
• Workloads optimized for FP32/FP16 precision, but could benefit from the cores that accelerate AI workloads and support multiple precisions.
• Running batch inference, image processing, or multi-tenant training.
• High memory capacity at a more accessible price point.
• Supporting a broader range of AI and HPC workloads.

NVIDIA H100

Containing a blend of CUDA and tensor cores, the H100 is better suited for:

• Training massive language models or diffusion models and need cutting-edge speed.
• Workloads which can leverage FP8 precision and the Transformer Engine.
• Data-intensive tasks need maximum bandwidth.
• ‘Future-proofing’ AI infrastructure for the next 2–4 years.

Performance and Memory Bandwidth Comparison for AI vs Non-AI Workloads

3 Key Considerations: Power, Efficiency and Compliance

Developers building AI infrastructure know raw performance isn’t the only thing that matters. Power draw directly affects thermal design, rack density, and operating costs. 

Efficiency determines how quickly and cost-effectively models can train or serve. 

Compliance matters for production workloads that handle sensitive data or run in regulated environments. 

Understanding how GPUs differ across these dimensions will guide your choices about architecture and deployment strategy.

Power Consumption

The NVIDIA H100 draws up to 700W, nearly double the A100’s 400W. That power fuels a jump in compute capacity and memory bandwidth. For power-hungry workloads like training trillion-parameter models or serving high-throughput inference, the H100 finishes tasks faster, cutting total energy consumed per job.

Multi-Instance GPU (MIG) support splits the GPU into up to seven isolated instances. Each instance runs independent workloads, which helps data centers consolidate compute while lowering power use per task. This makes the H100 more efficient at scale because it supports more MIG instances and more granular resource allocation.

When power budgets are tight or cooling infrastructure is limited, the A100 offers solid performance per watt for many AI and HPC use cases. Organizations pushing state-of-the-art model size or speed may prefer the H100’s higher power draw because it delivers significantly more performance headroom.

Efficiency

The H100 offers more efficiency when running modern AI models. Fourth-generation Tensor Cores and the dedicated Transformer Engine accelerate training and inference across large models. Comparatively it delivers up to 4× faster training speed. That speed cuts GPU hours required for each job, reducing power consumption and total cost.

The H100 also improves memory throughput and bandwidth, reducing bottlenecks common in large-scale training. Combined with NVIDIA’s AI Enterprise software stack, developers can allocate memory more effectively and keep GPUs fully utilized during compute-heavy operations.

MIG support improves operational efficiency by allowing multiple users or workloads to share a single physical GPU without interference. The H100 refines this feature, offering better resource distribution for AI labs, ML platforms, and multi-tenant environments.

Compliance

The H100 supports advanced hardware-based security features for compliance-heavy environments. It is a foundational part of the NVIDIA data center platform, which includes NVIDIA Confidential Computing. This protects data in use through secure enclaves, reducing exposure for sensitive workloads in healthcare, finance, and regulated industries.

With native support for isolation, encryption, and secure boot, the H100 aligns with modern security and privacy standards. These features make it a strong fit for compliance-driven workloads, where auditability and data protection matter as much as performance.

The A100 includes many of the same enterprise-grade protections but lacks the full range of hardware security modules and isolation technologies found in the H100. Organizations prioritizing compliance at scale will benefit from the H100’s expanded security architecture.

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Why the NVIDIA Data Center Platform Matters for GPU Cloud Users

If you’re running workloads on a GPU cloud, or sourcing compute from a neocloud provider like Voltage Park, your infrastructure should match your workload. The H100s Voltage Park owns unlock extreme performance for research, enterprise-scale AI training, and high-bandwidth compute.

Enterprises ready to optimize workloads, and build without the gamble of investing time and resources in creating an AI infrastructure from scratch can get started with Voltage Park today.

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