GPU Comparison

NVIDIA H100 vs H200

Compare NVIDIA H100 and H200 across GPU memory, bandwidth, Tensor Core performance, LLM inference, AI training and high-performance computing workloads.

Daya ShankarLast verified: August 11, 2026Research methodology

NVIDIA Hopper

H100

GPU Memory

80 GB

HBM3

Bandwidth

3.35

TB/s

A strong Hopper-generation GPU for AI training, inference and HPC when 80 GB of GPU memory is sufficient for the workload.

Explore NVIDIA H100
VS

NVIDIA Hopper

H200

GPU Memory

141 GB

HBM3e

Bandwidth

4.8

TB/s

A memory-enhanced Hopper GPU designed for workloads that benefit from substantially more GPU memory and higher memory bandwidth.

Explore NVIDIA H200

H100 vs H200 at a Glance

The decision is primarily about whether your workload benefits enough from H200's additional memory capacity and bandwidth.

H100

Choose H100 when

80 GB of GPU memory is enough for your workload and the H100 option available from your provider gives you the better overall cost.

H200

Choose H200 when

Your workload benefits from more GPU memory, higher memory bandwidth, larger inference batches or reduced memory pressure.

Tie

Compute is similar

Both use Hopper architecture and NVIDIA lists the same core SXM Tensor Core compute figures for H100 and H200.

NVIDIA H100 vs H200 Specifications

This table compares the SXM versions of H100 and H200 to keep the comparison consistent.

SpecificationNVIDIA H100 SXMNVIDIA H200 SXM
ArchitectureNVIDIA HopperNVIDIA Hopper
GPU Memory80 GB HBM3141 GB HBM3e
Memory Bandwidth3.35 TB/s4.8 TB/s
FP6434 TFLOPS34 TFLOPS
FP64 Tensor Core67 TFLOPS67 TFLOPS
FP3267 TFLOPS67 TFLOPS
TF32 Tensor Core*989 TFLOPS989 TFLOPS
FP16 / BF16 Tensor Core*1,979 TFLOPS1,979 TFLOPS
FP8 Tensor Core*3,958 TFLOPS3,958 TFLOPS
NVLink900 GB/s900 GB/s
MIGUp to 7 MIGs @ 10 GBUp to 7 MIGs @ 18 GB
Maximum TDPUp to 700 WUp to 700 W

* Tensor Core figures shown with sparsity, following NVIDIA's published specification tables.

What Is the Main Difference Between NVIDIA H100 and H200?

The biggest difference between H100 and H200 is not the underlying compute architecture. Both GPUs are based on NVIDIA Hopper and their SXM versions have the same listed FP64, FP32, TF32, FP16, BF16 and FP8 compute specifications.

H200 instead expands the memory subsystem. H100 SXM provides80 GB of HBM3 memory with3.35 TB/s of memory bandwidth. H200 SXM increases this to 141 GB of HBM3e with4.8 TB/s of memory bandwidth.

That gives H200 about 76% more GPU memory and roughly 43% more memory bandwidth. The difference matters most when model weights, KV cache, batch size or working datasets begin pushing against the memory limits of H100.

Memory Is Where H200 Pulls Ahead

H200 increases both memory capacity and the rate at which data can move to and from that memory.

GPU Memory

80 GB → 141 GB
H100 · 80 GB HBM3H200 · 141 GB HBM3e

Memory Bandwidth

3.35 → 4.8 TB/s
H100 · 3.35 TB/sH200 · 4.8 TB/s

H100 vs H200 for LLM Inference

H200 has the clearest advantage in large-model inference. LLM inference can consume substantial GPU memory for model weights, attention state and KV cache, especially as context lengths, concurrent requests and batch sizes increase.

H200's 141 GB of memory gives inference teams more room to hold model state on each GPU. Its 4.8 TB/s memory bandwidth can also improve throughput when the workload is limited by movement of data rather than raw Tensor Core compute.

H100 remains highly capable for inference when the model and desired serving configuration fit comfortably within its 80 GB memory envelope. In those cases, the additional memory of H200 may not justify a higher cloud price.

H100 vs H200 for AI Training

H100 and H200 are both high-end Hopper GPUs for AI training. Their listed SXM Tensor Core compute specifications are the same, so H200 should not be treated as a straightforward compute-generation upgrade over H100.

H200 becomes more attractive when training is constrained by GPU memory capacity or memory bandwidth. Additional memory can allow larger batches, larger model states or fewer compromises in how data and model components are distributed across GPUs.

If your training workload already fits efficiently on H100 and is primarily compute-bound, compare actual cloud pricing before assuming that moving to H200 will improve cost efficiency.

H100 vs H200 for HPC

Both GPUs provide the same listed 34 TFLOPS of FP64 performance and 67 TFLOPS of FP64 Tensor Core performance in their SXM variants.

The H200 advantage appears when HPC applications are sensitive to memory capacity or memory bandwidth. Scientific simulations, analytics and other data-intensive workloads can benefit when more working data remains in GPU memory or when memory movement is a bottleneck.

For compute-bound workloads that fit comfortably within H100 memory, the performance difference can be less significant.

Which GPU Fits Your Workload?

Use this as directional guidance rather than a substitute for workload-specific benchmarking.

WorkloadH100H200Direction
Small to medium LLM inferenceExcellentExcellentH100 may be enough
Large LLM inferenceStrongBetter fitH200
Long-context inferenceStrongBetter fitH200
Large batch inferenceStrongBetter fitH200
AI trainingExcellentExcellentDepends on memory needs
Memory-intensive HPCExcellentBetter fitH200
Cost-sensitive Hopper deploymentConsider firstCompare pricingDepends on provider

Cloud Pricing

Compare Provider Pricing Before Choosing

There is no single cloud price for either GPU. Rates depend on the provider, region, server configuration, billing model and commitment period.

H100 providers

Compare H100 pricing

View H100

H200 providers

Compare H200 pricing

View H200

Final Decision

Should You Choose NVIDIA H100 or H200?

Choose NVIDIA H100 if:

  • 80 GB of GPU memory comfortably fits your workload.
  • You want Hopper-class compute without paying for memory you do not need.
  • Your workload is primarily compute-bound rather than memory-bound.
  • Your chosen cloud provider offers a better H100 price or deployment option.

Choose NVIDIA H200 if:

  • 80 GB is becoming a memory constraint.
  • You are serving large LLMs or long-context workloads.
  • Higher memory bandwidth can improve your memory-bound workload.
  • More memory per GPU can simplify your desired deployment or batching strategy.

NVIDIA H100 vs H200 FAQs

Common questions when comparing NVIDIA's two Hopper-generation data center GPUs.

Is NVIDIA H200 faster than H100?
Not in every workload. H100 and H200 use the same Hopper architecture and have the same listed SXM Tensor Core compute specifications. H200 gains 141 GB of HBM3e memory and 4.8 TB/s of bandwidth, which can improve performance when memory capacity or bandwidth is the limiting factor.
What is the main difference between H100 and H200?
The biggest difference is memory. H100 SXM has 80 GB of HBM3 at 3.35 TB/s, while H200 SXM has 141 GB of HBM3e at 4.8 TB/s. Their listed SXM compute specifications are otherwise very similar.
Is H200 better than H100 for LLM inference?
H200 is generally the stronger option for memory-intensive LLM inference because its larger and faster memory can support larger models, more KV cache and larger batches. H100 remains a strong option when 80 GB is sufficient.
Is H200 better than H100 for AI training?
It depends on the training workload. Both provide the same listed Hopper Tensor Core compute performance in their SXM versions. H200 becomes more attractive when additional memory capacity or bandwidth reduces memory constraints.
Do H100 and H200 use the same architecture?
Yes. Both NVIDIA H100 and H200 are based on the NVIDIA Hopper architecture. H200 primarily extends the platform with larger and faster HBM3e memory.
Which is cheaper to rent, H100 or H200?
Cloud pricing varies by provider, region, configuration and billing model. H100 and H200 rental prices should therefore be compared using current provider pricing rather than assuming one fixed market price.

Sources & Verification

Technical specifications on this page were checked against official NVIDIA product information.

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

GPU Profile

NVIDIA H100

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

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GPU Profile

NVIDIA H200

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

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