GPU Comparison

NVIDIA H100 vs B200

Compare NVIDIA H100 and B200 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 Blackwell

B200

GPU Memory

180 GB HBM3e

Bandwidth

Up to 8 TB/s

Blackwell-generation accelerator for frontier AI training, FP4/FP8 inference and large-scale AI systems.

Explore NVIDIA B200

H100 vs B200 at a Glance

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

Choose H100 when

80 GB is enough for the model and batch size.

Choose B200 when

You need substantially more memory per GPU.

Compare the full workload

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

NVIDIA H100 vs NVIDIA B200 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 B200
ArchitectureNVIDIA HopperNVIDIA Blackwell
GPU Memory80 GB HBM3180 GB HBM3e
Memory Bandwidth3.35 TB/sUp to 8 TB/s
FP3267 TFLOPS≈75 TFLOPS†
TF32 Tensor Core989 TFLOPS*≈2.25 PFLOPS*†
FP16 / BF16 Tensor Core1,979 TFLOPS*≈4.5 PFLOPS*†
FP8 Tensor Core3,958 TFLOPS*≈9 PFLOPS*†
FP4 Tensor CoreNot natively supported≈18 PFLOPS sparse / 9 PFLOPS dense†
NVLink900 GB/s1.8 TB/s
MIGUp to 7 MIGs @ 10 GBUp to 7 MIGs; 1g profile starts at 23 GB
Maximum PowerUp to 700 WUp to 1,000 W in DGX B200
Form FactorSXMSXM (HGX B200)

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

† B200 per-GPU compute figures are derived from NVIDIA's published 8-GPU HGX B200 totals by dividing by eight. Memory, bandwidth and NVLink figures are published per GPU.

What Is the Main Difference Between NVIDIA H100 and NVIDIA B200?

H100 and B200 are different GPU generations aimed at high-end data-center AI. H100 is based on Hopper, while B200 moves to Blackwell with fifth-generation Tensor Cores and native FP4 capabilities.

B200 increases memory from 80 GB HBM3 to 180 GB HBM3e and raises per-GPU memory bandwidth from 3.35 TB/s to as much as 8 TB/s. Its fifth-generation NVLink also doubles GPU-to-GPU bandwidth from 900 GB/s on H100 to 1.8 TB/s on HGX B200.

The practical result is that B200 is better positioned for larger models, memory-bound inference and next-generation low-precision AI. H100 remains a capable production accelerator when those Blackwell capabilities are unnecessary.

Memory Capacity and Bandwidth

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

GPU Memory

80 GB HBM3 vs 180 GB HBM3e
H100B200

Memory Bandwidth

3.35 TB/s vs Up to 8 TB/s
H100B200

H100 vs B200 for LLM Inference

B200 is the stronger technical fit for large and throughput-sensitive LLM inference. Its 180 GB HBM3e capacity can keep more model state and KV cache on each GPU, while up to 8 TB/s of memory bandwidth reduces pressure in memory-bound serving. Blackwell also adds native FP4 Tensor Core capability. H100 remains strong for models and serving configurations that fit well within 80 GB and do not require FP4.

H100 vs B200 for AI Training

For frontier training, B200 offers the newer Blackwell Tensor Core generation, more than twice H100's memory capacity and faster scale-up communication. H100 is still relevant for established Hopper clusters, validated training pipelines and workloads where the extra Blackwell capability does not justify a platform change.

H100 vs B200 for HPC and Multi-GPU Scale

Both GPUs target accelerated data centers, but B200 provides a much faster memory subsystem and fifth-generation NVLink at 1.8 TB/s per GPU in HGX B200. H100 remains a strong HPC accelerator with 900 GB/s NVLink and broad Hopper-era system support.

Which GPU Fits Your Workload?

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

WorkloadH100B200Direction
Large LLM inferenceStrongBest fitB200
Long-context / high-concurrency servingStrongBest fitB200
Frontier model trainingExcellentBest fitB200
Established Hopper training stackBest fitExcellentH100 may be simpler
Memory-bound HPCExcellentBest fitB200
Multi-GPU scale-upExcellentBest fitB200
Deployment where 80 GB is sufficientBest fitExcellentCompare provider economics

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

B200 providers

Compare B200 pricing

View B200

Final Decision

Should You Choose NVIDIA H100 or NVIDIA B200?

Choose NVIDIA H100 if:

  • 80 GB is enough for the model and batch size.
  • Your software and cluster stack is already qualified on Hopper.
  • You want a widely deployed high-end accelerator without requiring Blackwell-only FP4.
  • The available H100 cloud offer has better economics for your workload.

Choose NVIDIA B200 if:

  • You need substantially more memory per GPU.
  • Your inference stack can benefit from native FP4 or higher FP8 throughput.
  • Memory bandwidth is a major bottleneck.
  • You are building a new high-end multi-GPU training or inference platform.

H100 vs B200 FAQs

Common questions about choosing between these NVIDIA GPUs.

Which is better, NVIDIA H100 or B200?
Choose B200 for Blackwell-era FP4/FP8 AI, substantially more memory bandwidth and faster scale-up links. Choose H100 when Hopper already meets the workload and availability, software qualification or deployment economics matter more than maximum generation-level performance.
What is the main difference between H100 and B200?
H100 uses Hopper with 80 GB HBM3 and 3.35 TB/s memory bandwidth, while B200 uses Blackwell with 180 GB HBM3e and Up to 8 TB/s. Tensor Core generation, precision support, interconnects and power can also differ.
Is H100 or B200 better for LLM inference?
B200 is the stronger technical fit for large and throughput-sensitive LLM inference. Its 180 GB HBM3e capacity can keep more model state and KV cache on each GPU, while up to 8 TB/s of memory bandwidth reduces pressure in memory-bound serving. Blackwell also adds native FP4 Tensor Core capability. H100 remains strong for models and serving configurations that fit well within 80 GB and do not require FP4.
Which GPU is better for AI training, H100 or B200?
For frontier training, B200 offers the newer Blackwell Tensor Core generation, more than twice H100's memory capacity and faster scale-up communication. H100 is still relevant for established Hopper clusters, validated training pipelines and workloads where the extra Blackwell capability does not justify a platform change.
How much memory do H100 and B200 have?
NVIDIA H100 provides 80 GB HBM3, while NVIDIA B200 provides 180 GB HBM3e. 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 B200?
Cloud rental pricing for H100 and B200 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 B200

See specifications, cloud providers, pricing and workload fit for B200.

View B200