P100 vs T4

Compute throughput favors the P100 slightly: its 9.3 TFLOPS in FP16 and FP32 exceeds the T4's 8.1 TFLOPS by 15 percent, benefiting training workloads that rely on half-precision or single-precision operations. The identical FP16 to FP32 ratio on both GPUs simplifies mixed-precision strategies, yet the P100's edge supports faster iterations in model training.

Memory bandwidth profoundly impacts real-world usage: the P100's 732 GB/s enables larger batch sizes in data-heavy tasks like LLM training, reducing overhead from memory bottlenecks. Conversely, the T4's 320 GB/s limits scalability for high-throughput scenarios, though its lower latency GDDR6 suits inference pipelines.

Power efficiency defines deployment choices. The T4's 70W TDP allows dense server packing, ideal for edge inference, while the P100's 250W demands robust cooling and higher electricity costs, offsetting its performance lead in sustained compute.

Select the P100 for bandwidth-intensive workloads. Its 732 GB/s memory bandwidth excels in training large models or scientific simulations requiring high data throughput: batch sizes double compared to the T4's 320 GB/s limit. At $0.07/hr minimum pricing, it delivers value for prolonged compute sessions.

Budget-conscious users prioritize the P100's average $0.25/hr rate over the T4's $1.66/hr, especially with NVLink interconnect for multi-GPU scaling unavailable on the T4.

Opt for the T4 in power-constrained environments. Its 70W TDP enables high-density deployments, fitting 4x more units per rack than the P100's 250W draw: this suits inference farms or edge computing.

The T4's Turing architecture from 2018 provides optimized INT8/INT4 for inference, complementing its 8.1 TFLOPS FP16, despite higher $0.53/hr starting price.