GPU Comparison

NVIDIA H100 vs A100

Compare NVIDIA H100 and A100 across architecture, GPU memory, bandwidth, AI inference, training, infrastructure requirements and practical workload fit.

Daya ShankarLast verified: August 11, 2026Research methodology

NVIDIA Hopper

H100

GPU Memory

80 GB HBM3

Bandwidth

3.35 TB/s

High-end Hopper accelerator for AI training, large-model inference and HPC.

Explore NVIDIA H100
VS

NVIDIA Ampere

A100

GPU Memory

80 GB HBM2e

Bandwidth

2.039 TB/s

Ampere data-center accelerator for AI training, inference, data analytics and HPC.

Explore NVIDIA A100

H100 vs A100 at a Glance

Start with workload fit, then validate the choice against the exact cloud configuration and pricing available to you.

Choose H100 when

You are training or serving transformer models at high throughput.

Choose A100 when

Your workload is already optimized and validated on Ampere.

Compare the full workload

Memory, bandwidth, precision, interconnects, media features, power and cloud price can all change the right answer.

NVIDIA H100 vs NVIDIA A100 Specifications

H100, H200 and A100 use SXM figures. B200 uses current NVIDIA HGX B200 specifications. L40S and L4 use their native PCIe card specifications, so each product is represented in its primary deployment form.

SpecificationNVIDIA H100NVIDIA A100
ArchitectureNVIDIA HopperNVIDIA Ampere
GPU Memory80 GB HBM380 GB HBM2e
Memory Bandwidth3.35 TB/s2.039 TB/s
FP3267 TFLOPS19.5 TFLOPS
TF32 Tensor Core989 TFLOPS*312 TFLOPS*
FP16 / BF16 Tensor Core1,979 TFLOPS*624 TFLOPS*
FP8 Tensor Core3,958 TFLOPS*Not natively supported
FP4 Tensor CoreNot natively supportedNot natively supported
NVLink900 GB/s600 GB/s
MIGUp to 7 MIGs @ 10 GBUp to 7 MIGs @ 10 GB
Maximum PowerUp to 700 W400 W standard SXM
Form FactorSXMSXM

* Tensor Core values marked with an asterisk are NVIDIA sparse specifications where applicable; dense performance is lower.

What Is the Main Difference Between NVIDIA H100 and NVIDIA A100?

H100 is the Hopper-generation successor to A100. Both can provide 80 GB of GPU memory, but H100 moves from HBM2e to HBM3 and lifts bandwidth from 2.039 TB/s on A100 SXM to 3.35 TB/s on H100 SXM.

The larger architectural shift is in AI compute. Hopper adds fourth-generation Tensor Cores and Transformer Engine support, including FP8. A100's Ampere Tensor Cores support TF32, BF16 and FP16 but do not provide H100's native FP8 path.

That makes H100 the more natural choice for modern transformer training and inference, while A100 remains useful for mature Ampere deployments, conventional deep learning and HPC.

Memory Capacity and Bandwidth

These specifications affect model fit, KV-cache headroom, batch size and memory-bound workloads.

GPU Memory

80 GB HBM3 vs 80 GB HBM2e
H100A100

Memory Bandwidth

3.35 TB/s vs 2.039 TB/s
H100A100

H100 vs A100 for LLM Inference

H100 is better suited to current LLM inference because Hopper introduces Transformer Engine and native FP8 Tensor Core support. Its higher HBM bandwidth also helps memory-intensive serving. A100 can still serve many quantized and smaller models effectively, especially when cloud pricing makes it attractive.

H100 vs A100 for AI Training

H100 provides a large Tensor Core throughput increase over A100 and supports FP8 through Hopper's Transformer Engine. A100 remains a proven training GPU for FP16/BF16 and TF32 workflows, but it represents the previous architecture generation.

H100 vs A100 for HPC

Both offer high-bandwidth HBM, MIG and NVLink. H100 raises FP64, FP32, Tensor Core and memory-bandwidth ceilings, while A100 remains a capable HPC accelerator with 80 GB HBM2e and 600 GB/s NVLink.

Which GPU Fits Your Workload?

Use this as directional guidance. Benchmark your own model and software stack before making a large infrastructure commitment.

WorkloadH100A100Direction
LLM inferenceExcellentStrongH100
Transformer trainingBest fitStrongH100
FP8 workloadsSupportedNot nativeH100
Traditional FP16/BF16 trainingBest fitExcellentH100
HPCBest fitExcellentH100
MIG-based sharingExcellentExcellentTie
Cost-sensitive established deploymentCompare pricingConsider firstDepends on provider

Cloud Pricing

Compare Current Provider Pricing

There is no single cloud price for either GPU. Rates vary by provider, region, server configuration, billing model and commitment. Compare current provider offers after you know which hardware class fits the workload.

H100 providers

Compare H100 pricing

View H100

A100 providers

Compare A100 pricing

View A100

Final Decision

Should You Choose NVIDIA H100 or NVIDIA A100?

Choose NVIDIA H100 if:

  • You are training or serving transformer models at high throughput.
  • FP8 support matters to your software stack.
  • You need higher HBM bandwidth or faster NVLink.
  • You are building a new high-performance AI cluster.

Choose NVIDIA A100 if:

  • Your workload is already optimized and validated on Ampere.
  • 80 GB HBM2e is sufficient.
  • You do not need native FP8.
  • A100 offers a materially better provider price for the performance you need.

H100 vs A100 FAQs

Common questions about choosing between these NVIDIA GPUs.

Which is better, NVIDIA H100 or A100?
Choose H100 for transformer-heavy generative AI, FP8 inference, higher memory bandwidth and faster Hopper Tensor Cores. Choose A100 for established Ampere workloads when 80 GB is enough and the older platform delivers better economics.
What is the main difference between H100 and A100?
H100 uses Hopper with 80 GB HBM3 and 3.35 TB/s memory bandwidth, while A100 uses Ampere with 80 GB HBM2e and 2.039 TB/s. Tensor Core generation, precision support, interconnects and power can also differ.
Is H100 or A100 better for LLM inference?
H100 is better suited to current LLM inference because Hopper introduces Transformer Engine and native FP8 Tensor Core support. Its higher HBM bandwidth also helps memory-intensive serving. A100 can still serve many quantized and smaller models effectively, especially when cloud pricing makes it attractive.
Which GPU is better for AI training, H100 or A100?
H100 provides a large Tensor Core throughput increase over A100 and supports FP8 through Hopper's Transformer Engine. A100 remains a proven training GPU for FP16/BF16 and TF32 workflows, but it represents the previous architecture generation.
How much memory do H100 and A100 have?
NVIDIA H100 provides 80 GB HBM3, while NVIDIA A100 provides 80 GB HBM2e. Memory capacity alone does not determine performance, so bandwidth, precision support and workload behavior should also be considered.
Which is cheaper to rent, H100 or A100?
Cloud rental pricing for H100 and A100 varies by provider, region, configuration and billing model. Check current provider pricing rather than assuming one GPU is always cheaper.

Sources & Verification

Hardware specifications were checked against official NVIDIA product pages and documentation.

Last verified: August 11, 2026 · View our data source standards

GPU Profile

NVIDIA H100

See specifications, cloud providers, pricing and workload fit for H100.

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GPU Profile

NVIDIA A100

See specifications, cloud providers, pricing and workload fit for A100.

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