A100 PCIe 80GB vs B300 SXM6

The B300 vastly outpaces the A100 in compute: its 2250 TFLOPS FP16 rating delivers 7.2 times the A100's 312 TFLOPS, accelerating mixed-precision training for deep learning models. FP32 performance reaches 90 TFLOPS on B300 versus 19.5 TFLOPS on A100, a 4.6-fold increase that benefits scientific simulations requiring single-precision arithmetic. FP8 at 4500 TFLOPS on B300 enables ultra-efficient inference for quantized models.

Memory specs define workload feasibility: B300's 288 GB HBM3e supports models up to hundreds of billions of parameters, while A100's 80 GB HBM2e limits to smaller batches. The 12000 GB/s bandwidth on B300, compared to 2039 GB/s on A100, reduces data movement bottlenecks, allowing 2-3 times larger batch sizes in training and slashing time-to-convergence.

Power draw reflects capabilities: B300's 1200W TDP demands robust cooling versus A100's 400W, impacting cluster efficiency. Real-world training runs scale near-linearly with these metrics, per benchmarks.

The A100 PCIe 80GB suits budget-conscious deployments where availability trumps peak performance. With pricing from $0.89 per hour across 28 offers, it undercuts B300's $2.45 per hour start, enabling cost savings of over 60 percent for inference or fine-tuning on models under 70 billion parameters. Its PCIe form factor integrates easily into existing datacenters without NVSwitch requirements.

Legacy software optimized for Ampere runs natively on A100, avoiding Blackwell compatibility hurdles. Lower 400W TDP fits power-constrained environments.

The B300 SXM6 excels in frontier AI research demanding massive scale. Its 288 GB VRAM handles trillion-parameter models infeasible on A100's 80 GB, while 12000 GB/s bandwidth supports enormous batch sizes for faster training convergence.

For production inference, 4500 TFLOPS FP8 throughput delivers sub-millisecond latencies on large deployments, justifying $6.44 per hour average cost through 7.2 times FP16 gains over A100.