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AI and High-Performance Computing (HPC) workloads are growing more complex, requiring hardware that can keep up with massive processing demands. NVIDIA’s GPUs have become a key part of this, powering everything from scientific research to the development of large language models (LLMs) worldwide.
Two of NVIDIA’s most significant accelerators are the A100 and the H100. The A100, launched in 2020 with the Ampere architecture, brought a major leap in compute density and flexibility, supporting analytics, training, and inference. In 2022, NVIDIA introduced the H100, built on the Hopper architecture, with an even bigger performance boost, especially for transformer-based AI workloads.
This blog provides a detailed comparison of the NVIDIA A100 and H100 GPUs, covering their architectural differences, core specifications, performance benchmarks, and best-fit applications to help you choose the right one for your needs.
The shift from NVIDIA’s Ampere to Hopper architectures represents a major step forward in GPU design, driven by the growing demands of modern AI and HPC workloads.
Released in 2020, the A100 GPU was designed as a flexible accelerator for a wide range of AI and HPC tasks. It introduced Multi-Instance GPU (MIG) technology, allowing a single GPU to be split into up to seven isolated instances, improving hardware utilization.
The A100 also featured third-generation Tensor Cores, which significantly boosted deep learning performance. With Tensor Float 32 (TF32) precision, it delivered much faster training and inference without requiring code changes. Its updated NVLink doubled GPU-to-GPU bandwidth to 600 GB/s, far exceeding PCIe Gen 4, enabling faster inter-GPU communication.
Launched in 2022, the H100 was built to meet the needs of large-scale AI, especially transformer and LLM workloads. It uses a 5 nm process with 80 billion transistors and introduces fourth-generation Tensor Cores along with the Transformer Engine using FP8 precision, enabling faster and more memory-efficient training and inference for trillion-parameter models without sacrificing accuracy.
For broader workloads, the H100 introduces several key upgrades: DPX instructions for accelerating Dynamic Programming algorithms, Distributed Shared Memory that allows direct communication between Streaming Multiprocessors (SMs), and Thread Block Clusters for more efficient task execution. The second-generation Multi-Instance GPU (MIG) architecture triples compute capacity and doubles memory per instance, while Confidential Computing provides secure enclaves for processing sensitive data.
These architectural changes deliver up to six times the performance of the A100 through a combination of more SMs, faster Tensor Cores, FP8 optimizations, and higher clock speeds. The result is a GPU that is not only faster but also purpose-built for today’s demanding AI and HPC applications.
| Feature | NVIDIA A100 (Ampere) | NVIDIA H100 (Hopper) |
| Architecture Name | Ampere | Hopper |
| Release Year | 2020 | 2022 |
| Tensor Cores Generation | 3rd Generation | 4th Generation |
| Transformer Engine | No | Yes (with FP8 support) |
| DPX Instructions | No | Yes |
| Distributed Shared Memory | No | Yes |
| Thread Block Cluster | No | Yes |
| MIG Generation | 1st Generation | 2nd Generation |
| Confidential Computing | No | Yes |
Examining the core specifications of the NVIDIA A100 and H100 highlights how the H100 improves on its predecessor in memory, bandwidth, interconnects, and compute power.
The A100 is based on the Ampere architecture (GA100 GPU), while the H100 uses the newer Hopper architecture (GH100 GPU). Built on a 5nm process, the H100 packs about 80 billion transistors, giving it greater compute density and efficiency.
The A100 was available in 40GB (HBM2) and 80GB (HBM2e) versions, offering up to 2TB/s of memory bandwidth. The H100 upgrades to 80GB of HBM3 in both SXM5 and PCIe versions, along with a 96GB HBM3 option for PCIe. Its memory bandwidth reaches 3.35TB/s, nearly double that of the A100. This increase allows the H100 to process larger models, use bigger batch sizes, and support more simultaneous sessions while reducing memory bottlenecks in AI workloads.
The A100 featured next-generation NVLink with 600GB/s GPU-to-GPU bandwidth. The H100 advances this to fourth-generation NVLink, increasing bandwidth to 900GB/s for better multi-GPU scaling. PCIe support also improves, moving from Gen4 (A100) to Gen5 (H100), effectively doubling system connection speeds.
The A100 80GB (SXM) includes 6,912 CUDA cores and 432 Tensor Cores. The H100 (SXM5) jumps to 16,896 CUDA cores and 528 Tensor Cores, along with a larger 50MB L2 cache (versus 40MB in the A100). These changes deliver significantly higher throughput for compute-heavy workloads.
The A100’s TDP ranged from 250W (PCIe) to 400W (SXM). The H100 draws more power, up to 700W for some variants, but offers much higher performance per watt — up to 3x more than the A100. This efficiency means lower energy use per task, reducing operating costs and easing data center power and cooling demands.
Both GPUs support MIG, letting a single GPU be split into up to seven isolated instances. The H100’s second-generation MIG triples compute capacity and doubles memory per instance, improving flexibility for mixed workloads.
Both GPUs are available in PCIe and SXM form factors. SXM versions provide higher bandwidth and better scaling, while PCIe models offer broader compatibility and lower costs.
The architectural differences between the A100 and H100 lead to major performance gaps across deep learning and high‑performance computing workloads.
The H100 delivers notable speedups in training, especially for large models. It provides up to 2.4× higher throughput than the A100 in mixed‑precision training and up to 4× faster training for massive models like GPT‑3 (175B). Independent testing shows consistent 2–3× gains for models such as LLaMA‑70B. These improvements are driven by the fourth‑generation Tensor Cores, FP8 precision, and overall architectural efficiency.
The H100 shows an even greater leap in inference performance. NVIDIA reports up to 30× faster inference for some workloads compared to the A100, while independent tests show 10–20× improvements. For LLMs in the 13B–70B parameter range, an A100 delivers about 130 tokens per second, while an H100 reaches 250–300 tokens per second. This boost comes from the Transformer Engine, FP8 precision, and higher memory bandwidth, allowing more concurrent requests with lower latency.
The reduced latency makes the H100 a strong choice for real‑time applications like conversational AI, code generation, and fraud detection, where response time is critical. In contrast, the A100 remains suitable for batch inference or background processing where latency is less important.
The H100 also outperforms the A100 in scientific computing. It increases FP64 performance from 9.7 TFLOPS on the A100 to 33.45 TFLOPS, with its double‑precision Tensor Cores reaching up to 60 TFLOPS. It also achieves 1 petaflop for single‑precision matrix‑multiply operations using TF32 with little to no code changes, cutting simulation times for research and engineering workloads.
Both GPUs support structural sparsity, which prunes less significant weights in a neural network in a structured pattern that GPUs can efficiently skip at runtime. This reduces FLOPs and improves throughput with minimal accuracy loss. The H100 refines this implementation, offering higher efficiency and better performance for both training and inference.
NVIDIA estimates the H100 delivers roughly 6× more compute performance than the A100. This is the result of a 22% increase in SMs, faster Tensor Cores, FP8 precision with the Transformer Engine, and higher clock speeds. These combined architectural improvements provide far greater real‑world gains than raw TFLOPS alone suggest, making the H100 a purpose‑built accelerator for the most demanding AI and HPC tasks.
Choosing between the A100 and H100 comes down to workload demands and cost. The A100 is a practical choice for teams prioritizing cost efficiency over speed. It performs well for training and inference where latency is not critical and can handle large models at a lower hourly cost.
The H100 is designed for performance at scale. With its Transformer Engine, FP8 precision, and higher memory bandwidth, it is significantly faster for large language models, generative AI, and complex HPC workloads. Its advantages are most apparent in real time inference and large scale training, where faster runtimes and reduced latency can translate to major operational savings even with a higher per hour cost.
For high performance, low latency workloads, or large model training at scale, the H100 is the clear choice. For less demanding tasks where cost takes priority, the A100 remains a strong and cost effective option.
If you are looking to deploy your own AI workloads on A100 or H100, you can do that using compute orchestration. More to the point, you are not tied to a single provider. With a cloud‑agnostic setup, you can run on dedicated infrastructure across AWS, GCP, Oracle, Vultr, and others, giving you the flexibility to choose the right GPUs at the right price. This avoids vendor lock‑in and makes it easier to switch between providers or GPU types as your requirements evolve
For a breakdown of GPU costs and to compare pricing across different deployment options, visit the Clarifai Pricing page. You can also join our Discord channel anytime to connect with AI experts, get your questions answered about choosing the right GPU for your workloads, or get help optimizing your AI infrastructure.
