Below is a detailed comparison of NVIDIA GPUs' performance in processing various precisions, including FP32, FP16, BF16, INT8, and others. These metrics depend on the GPU's architecture, Tensor Core capabilities, and optimization for specific workloads. I'll outline the performance for the GPUs you mentioned (RTX 4090, 4080, 4070, 4060, 4050, 3090, 2080, 1080, and 1050) where data is available.

Key GPU Performance Metrics by Precision

Explanation of Each Metric

1. FP32 (Single-Precision Floating Point - 32-bit)

   • FP32 is the default precision for many workloads, and modern GPUs are optimized to deliver high performance in FP32.
   • The RTX 4090 delivers 82.6 TFLOPS of FP32 performance, which is more than 9x the GTX 1080's 8.9 TFLOPS.

2. FP16 (Half-Precision Floating Point - 16-bit)

   • FP16 is widely used in AI and deep learning to accelerate training and inference while reducing memory usage. Tensor Cores introduced in Turing (RTX 20-series) boosted FP16 performance significantly.
   • RTX 4090 achieves 165.2 TFLOPS of FP16 performance, compared to just 26.9 TFLOPS for the RTX 2080.

3. BF16 (Brain Floating Point - 16-bit)

   • BF16 is supported starting from the Ampere architecture (RTX 30-series) and offers similar performance to FP16 but with a larger dynamic range.
   • RTX 4090 and RTX 4080 both deliver 165.2 TFLOPS and 97.4 TFLOPS, respectively, for BF16 workloads.

4. INT8 (8-bit Integer)

   • INT8 precision is primarily used in inference tasks, where lower precision can accelerate computation without large accuracy losses.
   • RTX 4090 delivers an industry-leading 1,321 TOPS of INT8 performance, compared to 383 TOPS for the RTX 3090 and 130 TOPS for the RTX 2080.

5. INT4 (4-bit Integer)

   • INT4 is used for ultra-lightweight inference tasks, such