L40 vs Quadro P4000

Compute performance defines the primary advantage of the L40: its 90.5 TFLOPS in FP16 and FP32 enables training and inference speeds roughly 17 times higher than the P4000's 5.3 TFLOPS. For machine learning training, this FP32 throughput accelerates gradient computations on large datasets. Inference benefits similarly, as FP16 tensor operations process more queries per second on the L40.

VRAM capacity impacts model handling directly: the L40's 48 GB supports large language models or high-resolution generative tasks that exceed the P4000's 8 GB limit. Memory bandwidth of 864 GB/s on the L40 sustains larger batch sizes by minimizing data transfer delays, unlike the P4000's 243 GB/s which constrains throughput in bandwidth-bound scenarios. Power draw reflects this, with the L40 at 300W TDP versus the P4000's 105W, indicating higher performance density.

Both GPUs use PCIe form factors without specified interconnects, but the L40's specs suit distributed training better due to superior raw metrics.

The L40 excels in demanding AI and HPC environments. Its 48 GB GDDR6 VRAM accommodates large models during LLM training or Stable Diffusion generation, where the P4000's 8 GB falls short. With 90.5 TFLOPS FP32 performance and 864 GB/s bandwidth, it processes complex workloads efficiently at $0.67 per hour starting price.

The Quadro P4000 suits legacy or low-intensity professional tasks. Its 105W TDP and $0.51 per hour pricing appeal for power-constrained setups or basic CAD rendering, where 5.3 TFLOPS suffices. Users with 8 GB memory needs avoid overprovisioning on the L40's 300W and higher costs.