GPU Comparison

NVIDIA B200 vs A100

Compare NVIDIA B200 and A100 across architecture, GPU memory, bandwidth, AI inference, training, infrastructure requirements and practical workload fit.

Daya ShankarLast verified: August 11, 2026Research methodology

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
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

B200 vs A100 at a Glance

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

Choose B200 when

You are building a new frontier AI platform.

Choose A100 when

Your workload is already qualified on Ampere.

Compare the full workload

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

NVIDIA B200 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 B200NVIDIA A100
ArchitectureNVIDIA BlackwellNVIDIA Ampere
GPU Memory180 GB HBM3e80 GB HBM2e
Memory BandwidthUp to 8 TB/s2.039 TB/s
FP32≈75 TFLOPS†19.5 TFLOPS
TF32 Tensor Core≈2.25 PFLOPS*†312 TFLOPS*
FP16 / BF16 Tensor Core≈4.5 PFLOPS*†624 TFLOPS*
FP8 Tensor Core≈9 PFLOPS*†Not natively supported
FP4 Tensor Core≈18 PFLOPS sparse / 9 PFLOPS dense†Not natively supported
NVLink1.8 TB/s600 GB/s
MIGUp to 7 MIGs; 1g profile starts at 23 GBUp to 7 MIGs @ 10 GB
Maximum PowerUp to 1,000 W in DGX B200400 W standard SXM
Form FactorSXM (HGX B200)SXM

* 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 B200 and NVIDIA A100?

B200 represents two major architecture steps beyond A100: Blackwell versus Ampere. It increases memory from 80 GB HBM2e to 180 GB HBM3e and raises bandwidth from 2.039 TB/s to up to 8 TB/s.

Blackwell fifth-generation Tensor Cores add native FP4 and much higher low-precision AI throughput, while A100's third-generation Tensor Cores do not provide native FP8 or FP4.

B200 is the performance-oriented choice for new large-model systems. A100 remains a practical accelerator for mature workloads that do not need Blackwell's memory or precision capabilities.

Memory Capacity and Bandwidth

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

GPU Memory

180 GB HBM3e vs 80 GB HBM2e
B200A100

Memory Bandwidth

Up to 8 TB/s vs 2.039 TB/s
B200A100

B200 vs A100 for LLM Inference

B200 is designed for the current era of large generative models. Its 180 GB HBM3e, up to 8 TB/s bandwidth and native FP4 create far more headroom for large model inference. A100 can still serve many smaller or quantized models.

B200 vs A100 for AI Training

B200 provides newer Tensor Cores, more than double A100's memory and substantially faster scale-up links. A100 remains a proven training accelerator for FP16/BF16 and TF32, especially in existing clusters.

B200 vs A100 for HPC and Scale-Up

A100 offers 600 GB/s NVLink and strong HBM2e bandwidth. B200 raises GPU-to-GPU NVLink bandwidth to 1.8 TB/s in HGX B200 and memory bandwidth to up to 8 TB/s.

Which GPU Fits Your Workload?

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

WorkloadB200A100Direction
Frontier LLM inferenceBest fitLegacy generationB200
FP4 / FP8 inferenceBest fitNot nativeB200
Large-model trainingBest fitStrongB200
HPC with large working setsBest fitExcellentB200
MIG multi-tenancyExcellentExcellentBoth
Existing Ampere clusterExcellentBest fitA100
Power-limited data centerHigher powerLower powerA100 may fit better

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.

B200 providers

Compare B200 pricing

View B200

A100 providers

Compare A100 pricing

View A100

Final Decision

Should You Choose NVIDIA B200 or NVIDIA A100?

Choose NVIDIA B200 if:

  • You are building a new frontier AI platform.
  • You need native FP4 or FP8.
  • 80 GB is no longer enough.
  • Scale-up communication and memory bandwidth are critical.

Choose NVIDIA A100 if:

  • Your workload is already qualified on Ampere.
  • 80 GB is sufficient.
  • You do not need FP8/FP4.
  • A100's lower platform requirements or provider price are more important.

B200 vs A100 FAQs

Common questions about choosing between these NVIDIA GPUs.

Which is better, NVIDIA B200 or A100?
Choose B200 for new frontier AI systems, native FP4/FP8, 180 GB memory and fifth-generation NVLink. Choose A100 for established Ampere workloads when its 80 GB HBM2e, mature software base and potentially lower provider cost are sufficient.
What is the main difference between B200 and A100?
B200 uses Blackwell with 180 GB HBM3e and Up to 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 B200 or A100 better for LLM inference?
B200 is designed for the current era of large generative models. Its 180 GB HBM3e, up to 8 TB/s bandwidth and native FP4 create far more headroom for large model inference. A100 can still serve many smaller or quantized models.
Which GPU is better for AI training, B200 or A100?
B200 provides newer Tensor Cores, more than double A100's memory and substantially faster scale-up links. A100 remains a proven training accelerator for FP16/BF16 and TF32, especially in existing clusters.
How much memory do B200 and A100 have?
NVIDIA B200 provides 180 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, B200 or A100?
Cloud rental pricing for B200 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 B200

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

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GPU Profile

NVIDIA A100

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

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