GPU Comparison

NVIDIA H200 vs A100

Compare NVIDIA H200 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

H200

GPU Memory

141 GB HBM3e

Bandwidth

4.8 TB/s

Memory-enhanced Hopper accelerator for large LLM inference, training and memory-intensive HPC.

Explore NVIDIA H200

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

H200 vs A100 at a Glance

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

Choose H200 when

You need more than 80 GB per GPU.

Choose A100 when

80 GB is sufficient.

Compare the full workload

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

NVIDIA H200 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.

Specification	NVIDIA H200	NVIDIA A100
Architecture	NVIDIA Hopper	NVIDIA Ampere
GPU Memory	141 GB HBM3e	80 GB HBM2e
Memory Bandwidth	4.8 TB/s	2.039 TB/s
FP32	67 TFLOPS	19.5 TFLOPS
TF32 Tensor Core	989 TFLOPS*	312 TFLOPS*
FP16 / BF16 Tensor Core	1,979 TFLOPS*	624 TFLOPS*
FP8 Tensor Core	3,958 TFLOPS*	Not natively supported
FP4 Tensor Core	Not natively supported	Not natively supported
NVLink	900 GB/s	600 GB/s
MIG	Up to 7 MIGs @ 18 GB	Up to 7 MIGs @ 10 GB
Maximum Power	Up to 700 W	400 W standard SXM
Form Factor	SXM	SXM

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

What Is the Main Difference Between NVIDIA H200 and NVIDIA A100?

H200 and A100 are separated by both architecture and memory generation. A100 uses Ampere with 80 GB HBM2e, while H200 uses Hopper with 141 GB HBM3e.

Memory bandwidth rises from 2.039 TB/s on A100 80 GB SXM to 4.8 TB/s on H200 SXM. H200 also adds Hopper Transformer Engine and native FP8 support that A100 does not provide.

The result is a substantial advantage for H200 on modern generative AI and memory-heavy workloads. A100 remains relevant where its established software support and potentially lower cloud rates are sufficient.

Memory Capacity and Bandwidth

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

GPU Memory

141 GB HBM3e vs 80 GB HBM2e

H200A100

Memory Bandwidth

4.8 TB/s vs 2.039 TB/s

H200A100

H200 vs A100 for LLM Inference

H200 has 61 GB more GPU memory, more than twice A100's listed memory bandwidth and native FP8. That gives it much more room for large model weights, KV cache, longer contexts and larger serving batches.

H200 vs A100 for AI Training

H200 combines Hopper Tensor Cores with 141 GB HBM3e, while A100 uses third-generation Ampere Tensor Cores and 80 GB HBM2e. For modern transformer training, H200 is the stronger technical platform.

H200 vs A100 for HPC

Both support MIG and NVLink, but H200 provides substantially higher memory bandwidth and a larger memory pool. A100 continues to be a capable HPC accelerator for workloads that do not need H200's extra memory.

Which GPU Fits Your Workload?

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

Workload	H200	A100	Direction
Large LLM inference	Best fit	Strong	H200
Long-context serving	Best fit	Strong	H200
Transformer training	Best fit	Strong	H200
FP8 workloads	Supported	Not native	H200
HPC with large datasets	Best fit	Excellent	H200
MIG multi-tenancy	Excellent	Excellent	Both
Established Ampere deployment	Excellent	Best fit	A100

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.

H200 providers

Compare H200 pricing

View H200

A100 providers

Compare A100 pricing

View A100

Final Decision

Should You Choose NVIDIA H200 or NVIDIA A100?

Choose NVIDIA H200 if:

You need more than 80 GB per GPU.
Memory bandwidth is limiting performance.
Your stack benefits from Hopper FP8.
You are targeting large-model inference or memory-intensive training.

Choose NVIDIA A100 if:

80 GB is sufficient.
Your software estate is built around Ampere.
You do not need FP8.
A100 gives you better workload economics from the provider you use.

H200 vs A100 FAQs

Common questions about choosing between these NVIDIA GPUs.

Which is better, NVIDIA H200 or A100?

Choose H200 for large LLMs, memory-intensive inference, FP8 and higher-bandwidth training/HPC. Choose A100 for established Ampere workloads when 80 GB is sufficient and provider economics outweigh the newer Hopper capabilities.

What is the main difference between H200 and A100?

H200 uses Hopper with 141 GB HBM3e and 4.8 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 H200 or A100 better for LLM inference?

Which GPU is better for AI training, H200 or A100?

H200 combines Hopper Tensor Cores with 141 GB HBM3e, while A100 uses third-generation Ampere Tensor Cores and 80 GB HBM2e. For modern transformer training, H200 is the stronger technical platform.

How much memory do H200 and A100 have?

NVIDIA H200 provides 141 GB HBM3e, 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, H200 or A100?

Cloud rental pricing for H200 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 H200

See specifications, cloud providers, pricing and workload fit for H200.

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GPU Profile

NVIDIA A100

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

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