## Bar Chart: Latency Comparison Between FP16 and w8a8 Methods ### Overview The chart compares latency (in milliseconds) for two computational methods, **FP16** (gray bars) and **w8a8** (red bars), across four batch sizes: 128, 256, 512, and 1024. Latency increases with batch size for both methods, but FP16 exhibits steeper growth. ### Components/Axes - **X-axis (Batch Size)**: Labeled with values 128, 256, 512, 1024. - **Y-axis (Latency)**: Labeled "Latency (ms)" with a range from 0 to 200. - **Legend**: Located in the top-left corner, associating gray with FP16 and red with w8a8. - **Bars**: Paired bars for each batch size, with numerical labels on top. ### Detailed Analysis - **Batch Size 128**: - FP16: 28 ms - w8a8: 22 ms - **Batch Size 256**: - FP16: 44 ms - w8a8: 33 ms - **Batch Size 512**: - FP16: 87 ms - w8a8: 63 ms - **Batch Size 1024**: - FP16: 181 ms - w8a8: 125 ms ### Key Observations 1. **FP16 Latency**: Increases non-linearly, with a sharp jump from 87 ms (512) to 181 ms (1024). 2. **w8a8 Latency**: Grows more gradually, maintaining a ~20–30 ms advantage over FP16 at all batch sizes. 3. **Scalability**: FP16 latency scales poorly at larger batches (e.g., 1024 is 2.08x slower than 512), while w8a8 scales more efficiently (1024 is 1.89x slower than 512). ### Interpretation The data suggests **w8a8** is more scalable than FP16 for larger batch sizes, likely due to optimized precision handling (e.g., 8-bit quantization). FP16’s quadratic-like latency growth at 1024 indicates potential bottlenecks in memory or computation for high-precision operations. This trend is critical for applications prioritizing efficiency in large-scale processing.