A16 vs P100

Raw compute power favors the P100 decisively. Its 9.3 TFLOPS FP16 and FP32 ratings surpass the A16's 4.5 TFLOPS by more than double, accelerating half-precision training and inference workloads significantly. For deep learning tasks, this delta means the P100 processes matrix multiplications faster, reducing epoch times in models limited by floating-point operations.

Memory bandwidth profoundly impacts real-world usage: the P100's 732 GB/s enables larger batch sizes than the A16's 231 GB/s, crucial for memory-bound inference or training on datasets exceeding 16 GB VRAM thresholds. In scenarios like large language model inference, higher bandwidth minimizes data transfer bottlenecks, sustaining higher throughput.

Despite the A16's newer Ampere architecture, its PCIe form factor lacks the P100's NVLink interconnect or SXM2 option, limiting multi-GPU scaling. Both share 250W TDP, so power efficiency per TFLOP tilts toward the P100 at 0.027W per TFLOP versus the A16's 0.056W per TFLOP.

The A16 suits modern cloud workloads requiring broad software compatibility. Its 2021 Ampere architecture supports newer CUDA versions and tensor cores optimized for inference in virtual desktop infrastructure or light AI tasks. With 74 live pricing offers averaging $0.48 per hour, availability exceeds the P100's 3 offers.

Users prioritizing PCIe form factor simplicity over peak performance select the A16 for scalable deployments without NVLink dependencies.

The P100 excels in cost-sensitive, high-throughput compute environments. At $0.07 per hour starting price and 9.3 TFLOPS FP16 performance, it offers superior value for FP16-heavy training or scientific simulations. The 732 GB/s bandwidth supports memory-intensive batch processing unavailable at the A16's 231 GB/s level.

Legacy HPC setups benefit from NVLink interconnect and SXM2 form factor, enabling efficient multi-GPU configurations at lower average $0.25 per hour costs.