H100 vs A100: NVIDIA Data Center GPU Comparison

Introduction: Two Generations, Two Different Use Cases

On this page we compare two generations of NVIDIA’s data center GPU family: A100 (Ampere architecture, 2020) and H100 (Hopper architecture, 2022). Both are professional GPUs designed for HPC simulations, artificial intelligence training, and large-scale data processing; however, the architectural and performance differences between them directly influence investment decisions.

The A100 set a new standard for data center GPUs when it launched. As large language models (LLMs) became dominant workloads from 2022 onward, NVIDIA developed the H100 with the Hopper architecture. The H100 is not just faster — it is a GPU redesigned for AI-focused workloads. The right choice between these two cards depends on budget, workload type, and infrastructure context.

Architectural Differences

A100 — Ampere Architecture

The A100 is manufactured on TSMC’s 7 nm process and contains 54.2 billion transistors. It houses 6,912 CUDA cores and 432 Tensor Cores (3rd generation). With HBM2e memory, it reaches 80 GB capacity and 2 TB/s bandwidth. It supports PCIe and NVLink 3.0 connectivity options; with NVSwitch in 8-GPU DGX A100 configurations, a total GPU-to-GPU bandwidth of 600 GB/s is achieved.

One of the A100’s distinctive features is Multi-Instance GPU (MIG) technology. It allows a single GPU to be divided into 7 independent partitions, enabling multiple workloads to run simultaneously with hardware-level isolation. This feature is highly valuable for multi-user HPC environments and inference clusters.

H100 — Hopper Architecture

The H100 is manufactured on TSMC’s 4 nm N4 process and contains 80 billion transistors. It houses 16,896 CUDA cores and 528 Tensor Cores (4th generation, with FP8 support). The SXM5 version reaches 80 GB capacity with HBM3 memory and 3.35 TB/s bandwidth; GPU-to-GPU bandwidth rises to 900 GB/s with NVLink 4.0.

The H100’s most important architectural innovation is the Transformer Engine. This unit is designed to accelerate attention mechanism computations used in large language models at the hardware level. FP8 precision support maximizes throughput from memory bandwidth while maintaining precision. The improved MIG technology in the H100 offers more flexible partitioning policies than 7 fixed partitions.

Comparison Table

A100 Strengths

• Mature ecosystem: Has been widely used in the industry since 2020. CUDA libraries, drivers, and workload optimizations have been thoroughly tested for the A100.
• Cost efficiency: Offers a much lower initial cost compared to the H100 for similar FP64 compute requirements. The cost advantage per watt is significant for HPC cluster expansion.
• Traditional HPC simulations: For CFD (OpenFOAM, ANSYS Fluent), FEM (LS-DYNA, Abaqus), and molecular dynamics (GROMACS, AMBER) workloads, the A100 is often more advantageous than the H100 in terms of price/performance.
• High utilization with MIG: 7-partition support enables efficiently hosting small and medium-scale inference workloads on a single card.
• Lower power consumption: The 400 W TDP is a significant advantage over the H100’s 700 W in terms of energy costs and cooling infrastructure.

A100 Weaknesses

• FP8 precision is not supported; the highest possible throughput for large language models cannot be achieved.
• The absence of Transformer Engine means 2–3x lower throughput compared to the H100 for LLM training and inference.
• Memory bandwidth remains at approximately 60% of the H100’s; this can be limiting for rapidly moving large model parameters within GPU memory.
• Due to its 2020 technology, software support and driver updates may decrease in coming years.

H100 Strengths

• Clear superiority in LLM training: Provides 3–4x higher throughput than the A100 for training large language models like GPT, LLaMA, and Falcon. The Transformer Engine and FP8 support are the primary sources of this difference.
• High FP64 performance: With 34 TFLOPS, it also significantly outpaces the A100 (9.7 TFLOPS) in scientific computing. This difference is critically important in fields like quantitative finance and climate modeling where double-precision simulations are intensive.
• Memory bandwidth: 3.35 TB/s enables fast loading of large model parameters and low-latency inference.
• NVLink 4.0: In an 8-H100 DGX H100 system, 900 GB/s GPU-to-GPU bandwidth makes distributing very large models across multiple GPUs (model parallelism) efficient.
• Long-term investment security: NVIDIA prioritizes new software features and optimizations for the Hopper architecture.

H100 Weaknesses

• High cost: The price range of $30,000–$40,000 per card is 2–3x higher than the A100. Making investment decisions without ROI analysis carries risk.
• High power consumption: The 700 W TDP requires specialized cooling infrastructure, power distribution unit (PDU) capacity, and data center cooling planning.
• Overcapacity for FP64 workloads: If you only run traditional HPC simulations, you won’t benefit from the H100’s LLM-focused features (Transformer Engine, FP8); the cost differential goes unrecouped.
• Procurement difficulty: Due to high global demand, H100 procurement lead times can be longer than for the A100.

When to Use Which?

Choose H100 — If:

• You are doing large language model (LLM) training; for GPT-like models above 7B parameters, H100 becomes a practical necessity.
• You are providing high-throughput AI inference; when millisecond-level latency and high requests-per-second capacity are needed, the H100’s memory bandwidth is decisive.
• You run workloads supporting FP8 precision; for areas with precision tolerance like image classification, object detection, and recommendation systems, maximum throughput is achieved with FP8.
• You have scientific simulations requiring high FP64; the H100’s 34 TFLOPS FP64 capacity is a significant advantage for climate modeling, quantum chemistry, or double-precision financial computing workloads.
• You plan large-scale multi-GPU parallel training; the 900 GB/s bandwidth provided by NVLink 4.0 makes model parallelism efficient.

Choose A100 — If:

• You run traditional CFD/FEM workloads; tools like OpenFOAM, ANSYS Fluent, LS-DYNA, or GROMACS do not benefit from FP16/FP8 support. In this scenario, the A100 delivers sufficient performance at a much lower cost.
• Budget constraints are decisive and you have no LLM workloads; you can achieve similar compute capacity by building an A100 cluster at two to three times lower investment.
• You operate a multi-user environment with MIG; if workloads are small and varied, a 7-partition MIG configuration on a single A100 provides high resource utilization.
• Energy and cooling capacity is limited; the 400 W TDP can be easily integrated into existing data center infrastructure.
• You apply a refurbished hardware strategy; A100 is now available in the second-hand market at significantly reduced prices, making the TCO advantage strong.

Mixed Approach — Hybrid Cluster:

For organizations hosting multiple workload types, the most efficient solution is a hybrid cluster using different GPU generations side by side. The SLURM job scheduler can efficiently manage A100 and H100 nodes within the same cluster by routing incoming jobs based on GPU type and resource requirements. In this approach, AI/LLM workloads can be directed to H100 nodes, while traditional HPC simulations go to A100 nodes.

GPU Selection with Mevasis

The choice between H100 and A100 can result in several times the cost difference depending on your budget and workload profile. The wrong choice means both overpaying and insufficient performance.

The Mevasis HPC team provides independent technical assessment on GPU selection, cluster architecture, and SLURM configuration by analyzing your current and planned workloads. Concrete configuration recommendations are developed for different budget and requirement scenarios — from refurbished A100 systems to H100-based AI clusters.

For a free technical assessment: contact us