Comparison

NVIDIA B200 vs NVIDIA A100 80GB

Two-generation leap — Blackwell vs Ampere

The B200 delivers 5.8x the FP16 performance (1,800 vs 312 TFLOPS) with 2.4x the memory (192GB vs 80GB). A two-generation jump in capability at a significant price premium.

Pricing Comparison

Specifications

Specification	NVIDIA B200	NVIDIA A100 80GB
Manufacturer	NVIDIA	NVIDIA
Architecture	Blackwell	Ampere
Accelerator Type	GPU	GPU
Primary Use	training	training
Memory (VRAM)	192 GB	80 GB
FP16 Performance	1800 TFLOPS	312 TFLOPS
TDP	1000W	400W
Perf per Watt	1.80 TFLOPS/W	0.78 TFLOPS/W

Detailed Analysis

The B200 and A100 80GB are separated by two GPU generations — Ampere to Hopper to Blackwell. The performance gap reflects this: 1,800 vs 312 FP16 TFLOPS, a 5.8x improvement.

The B200's 192GB of HBM3e memory more than doubles the A100's 80GB, while memory bandwidth increases from 2.0 TB/s to over 8 TB/s. This makes the B200 dramatically better for training and serving very large models.

Despite this, the A100 80GB remains relevant because of its cost advantage. At roughly one-quarter the hourly price of the B200, the A100 can be more cost-effective for workloads that don't require frontier-class performance.

The decision between them is primarily about scale. If you're training models above 30B parameters or need maximum throughput, the B200's performance advantage is transformative. For fine-tuning, smaller model training, and cost-optimised inference, the A100 remains an excellent choice.