P100 vs RTX 4060

Compute performance favors the RTX 4060: its 15.1 TFLOPS in FP16 and FP32 exceeds the P100's 9.3 TFLOPS, enabling 62 percent faster operations in training and inference for models leveraging half-precision arithmetic. This delta translates to quicker epoch times in deep learning, particularly for inference where low-latency responses matter. The Ada Lovelace architecture further enhances efficiency through improved tensor cores absent in Pascal.

Memory differences impact workload feasibility. The P100's 16 GB HBM2 allows larger batch sizes than the RTX 4060's 8 GB GDDR6, critical for training expansive models without out-of-memory errors. Higher 732 GB/s bandwidth on P100 accelerates data transfers, supporting bigger batches versus RTX 4060's 272 GB/s, which may bottleneck memory-intensive tasks like large language model fine-tuning.

Power consumption reveals efficiency gaps: P100 draws 250W TDP compared to RTX 4060's 115W, making the latter preferable for dense cloud deployments. Real-world training sees RTX 4060 complete small-to-medium workloads faster, while P100 excels in memory-bound scenarios despite lower peak throughput.

Opt for the P100 in scenarios demanding high memory capacity: its 16 GB HBM2 handles larger models and batch sizes than the RTX 4060's 8 GB GDDR6. NVLink interconnect enables efficient multi-GPU scaling for distributed training, unavailable on RTX 4060. At starting prices from $0.07/hr, it suits budget-conscious high-memory HPC or legacy workloads compatible with Pascal.

Select the RTX 4060 for compute-intensive tasks: 15.1 TFLOPS in FP16 and FP32 outperforms P100's 9.3 TFLOPS, accelerating training and inference. Lower 115W TDP and average $0.15/hr pricing across 6 offers favor efficient, modern deployments. Ada Lovelace architecture supports contemporary features for gaming-adjacent AI like Stable Diffusion.