The Ultimate GPU Showdown for AI & HPC!

An in-depth comparison between NVIDIA GPUs prior to the H100 and the NVIDIA H100 itself, we’ll focus on the key predecessor in NVIDIA’s data center lineup: the A100, which was the flagship GPU before the H100’s release in 2022. The A100, based on the Ampere architecture, set a high bar for AI, machine learning (ML), and high-performance computing (HPC) workloads.

The H100, built on the newer Hopper architecture, was designed to push those boundaries even further, particularly for large-scale AI models like transformers and advanced scientific simulations.

1. Architectural Differences

NVIDIA A100 (Ampere Architecture)

• Release Date: May 2020
• Process Node: 7nm (TSMC)
• Architecture: Ampere, NVIDIA’s 8th-generation data center GPU architecture.
• Key Features:

The Ampere architecture was a significant leap over its predecessor, the Volta-based V100, focusing on versatility for AI, HPC, and data analytics workloads.

NVIDIA H100 (Hopper Architecture)

• Release Date: March 2022
• Process Node: 4nm (TSMC)
• Architecture: Hopper, NVIDIA’s 9th-generation data center GPU architecture.
• Key Features:

The Hopper architecture is purpose-built for massive AI models and next-gen HPC, emphasizing scalability and efficiency over the A100’s broader versatility.

2. Technical Specifications

• Compute Power: The H100 significantly outclasses the A100 across all precision levels. For example, FP64 (double precision) jumps from 9.7 TFLOPS to 30 TFLOPS, and FP16 (half precision) with sparsity leaps from 312 TFLOPS to 989 TFLOPS. The addition of FP8 support in the H100 (1,979 TFLOPS) is a game-changer for AI workloads requiring high throughput with reduced precision.
• Memory: While both offer 80GB configurations, the H100’s HBM3 provides 67% more bandwidth (3.35 TB/s vs. 2 TB/s), critical for feeding data to its increased core count.
• Power Consumption: The H100’s PCIe version uses 350W (vs. 300W for A100 PCIe), while the SXM5 variant ramps up to 700W for maximum performance in high-density setups.

3. Performance Comparison

Benchmarks and Claims

• NVIDIA Claims: NVIDIA states the H100 delivers up to 9x faster AI training and 30x faster AI inference on large language models (LLMs) compared to the A100. These figures are based on optimized workloads (e.g., GPT-3) using FP8 precision and NVLink 4.0 scaling.
• Independent Benchmarks: Real-world tests temper these claims. For instance, MosaicML’s benchmarks show the H100 achieving ~3x faster training on LLMs compared to the A100, depending on model size and optimization. LambdaLabs reported lower gains (~2-3x) for a 175B-parameter GPT-3-like model with FlashAttention2.
• HPC Workloads: The H100’s 3x higher FP64 performance (30 TFLOPS vs. 9.7 TFLOPS) makes it far superior for scientific simulations, such as molecular dynamics or weather modeling.

Key Performance Drivers

• Transformer Engine: Exclusive to the H100, this boosts transformer model performance (e.g., BERT, GPT) by optimizing precision dynamically, a feature absent in the A100.
• NVLink Scaling: The H100’s 900 GB/s NVLink bandwidth (vs. 600 GB/s) enables up to 9x faster training in multi-GPU setups, as seen in NVIDIA’s cluster tests.
• Core Count: With over twice as many CUDA cores (14,592 vs. 6,912) and enhanced Tensor Cores, the H100 handles parallel tasks more efficiently.

4. Use Cases

A100

• Strengths: Versatile across AI training/inference, data analytics, and HPC. Ideal for organizations needing a balance of performance and cost.
• Applications:
• Limitations: Struggles with the largest transformer models (e.g., trillion-parameter LLMs) due to lower memory bandwidth and compute power compared to H100.

H100

• Strengths: Optimized for cutting-edge AI (especially LLMs) and extreme HPC workloads. Future-proof for next-gen applications.
• Applications:
• Limitations: Overkill for smaller workloads; higher power and cost may not justify use in less demanding scenarios.

5. Cost and Efficiency

• Cost:
• Cost Efficiency: The H100’s higher performance can offset its cost if workloads finish significantly faster. For example, a task taking 10 hours on an A100 might take 3-5 hours on an H100, potentially reducing total cloud billing despite the higher hourly rate.
• Power Efficiency: The A100 is more energy-efficient per watt for lighter workloads (300W vs. 350W-700W), but the H100’s performance-per-watt shines in optimized AI tasks, especially with FP8.

6. Other Pre-H100 GPUs

While the A100 is the primary predecessor, earlier GPUs like the V100 (Volta, 2017) and T4 (Turing, 2018) were also used in data centers:

• V100: 5,120 CUDA cores, 32GB HBM2, 900 GB/s bandwidth, 7.8 FP64 TFLOPS. The A100 was ~20x faster in some workloads, and the H100 further widens this gap (e.g., 4x FP64).
• T4: Focused on inference, with 2,560 CUDA cores and 320 GB/s bandwidth. It’s vastly outclassed by both A100 and H100 for training and HPC.

The H100’s leap over these older GPUs is even more pronounced, making direct comparisons less relevant for modern workloads.

Ctfuture Cloud

We are at the forefront of innovation, leveraging the cutting-edge NVIDIA H100 GPUs to deliver unparalleled AI training, inference, and high-performance computing (HPC) capabilities. Whether you're working on large-scale LLMs, deep learning, or complex scientific simulations, our H100-powered cloud infrastructure ensures superfast processing, seamless deployment, and optimal efficiency.

With H100’s superior performance in AI and HPC, Cyfuture Cloud empowers enterprises, researchers, and developers to push the boundaries of what's possible—reducing training times, improving scalability, and maximizing cost efficiency.

7. Conclusion: A100 vs. H100

• Choose the A100 if: You need a cost-effective, versatile GPU for mixed workloads (AI, HPC, analytics) and don’t require the latest transformer optimizations or extreme scalability. It remains highly capable in 2025 for many applications.
• Choose the H100 if: You’re tackling massive AI models (e.g., LLMs with billions/trillions of parameters), cutting-edge HPC, or need future-proofing. Its superior compute, memory bandwidth, and transformer focus make it the top choice for bleeding-edge tasks.

The H100 represents a generational shift over the A100 and earlier GPUs, excelling in raw power and AI-specific optimizations. However, its benefits are most pronounced in large-scale, optimized environments—smaller or less specialized workloads may not justify the investment. Always evaluate your specific needs, budget, and software stack to decide which GPU aligns best with your goals.