P100 vs RTX 4090

Performance gaps dominate comparisons: the RTX 4090 delivers 165 TFLOPS FP16 versus the P100s 9.3 TFLOPS, an 18-fold increase ideal for accelerating deep learning training. FP32 sees 82.6 TFLOPS on the RTX 4090 against 9.3 TFLOPS, nine times higher, benefiting simulations and general compute. The FP16 to FP32 ratio on the P100 remains 1:1, suiting balanced precision tasks from its era, while the RTX 4090s disparity favors half-precision training common today. FP8 at 660 TFLOPS on the RTX 4090 enables ultra-efficient inference for large language models. Memory bandwidth of 1008 GB/s on the RTX 4090 supports larger batch sizes than the P100s 732 GB/s, reducing overhead in data-heavy workloads. The 24 GB VRAM versus 16 GB allows bigger models without splitting, though the P100s 250W TDP contrasts the 450W draw, impacting dense deployments.

The P100 suits ultra-budget machine learning where costs dominate: from $0.07 per hour, it undercuts the RTX 4090s $0.16 per hour minimum. Legacy Pascal-optimized code runs natively without recompilation, preserving 9.3 TFLOPS FP32 for scientific computing or small models fitting 16 GB HBM2. Low 250W TDP enables power-constrained environments over the 450W RTX 4090.

The RTX 4090 excels in performance-critical AI: 165 TFLOPS FP16 drives faster LLM training than the P100s 9.3 TFLOPS. Its 24 GB VRAM and 1008 GB/s bandwidth handle larger batches and models, with FP8 at 660 TFLOPS optimizing inference. Abundant cloud availability across 104 offers ensures scalability despite higher average $0.47 per hour pricing.