A30 vs B300

The FP16 dense performance ratio of 2250 TFLOPS on the B300 to 165 TFLOPS on the A30 indicates substantially greater throughput for training workloads that rely on dense matrix operations. Inference tasks benefit similarly when comparing FP16 with sparsity where the B300 reaches 4500 TFLOPS against 330 TFLOPS on the A30. Memory bandwidth at 8000 GB/s versus 933 GB/s allows the B300 to sustain larger batch sizes during both training and inference phases. The FP32 figure of 75 TFLOPS on the B300 compared to 10.3 TFLOPS on the A30 further widens the gap for precision sensitive scientific computing. INT8 dense performance reverses the pattern with 330 TOPS on the A30 exceeding 187.5 TOPS on the B300 so quantization heavy inference may favor the older card in power constrained settings.

The A30 suits deployments where TDP remains limited to 165W and workloads fit within 24 GB of HBM2 memory. Organizations running INT8 dense operations at 330 TOPS gain efficiency from the lower power draw without requiring the 1400W TDP of newer hardware. PCIe form factor compatibility also favors the A30 in existing server infrastructures that lack SXM6 support.

The B300 addresses large scale LLM training and inference that demand 262 GB of HBM3e memory and 8000 GB/s bandwidth. FP16 dense throughput at 2250 TFLOPS combined with NVLink and InfiniBand interconnects enables scaling across multiple units for models that exceed A30 capacity. FP4 dense performance at 13500 TFLOPS further supports emerging low precision workloads unavailable on the A30.