## Bar Chart: Latency vs. Batch Size for FP16 and INT8 ### Overview The image is a bar chart comparing the latency (in milliseconds) of FP16 and INT8 data types across different batch sizes (1, 8, 16, and 32). The chart shows that latency generally increases with batch size for both data types, but INT8 consistently exhibits lower latency than FP16 for all batch sizes. ### Components/Axes * **Title:** Implicit, but the chart compares Latency vs. Batch Size for FP16 and INT8. * **X-axis:** Batch Size, with values 1, 8, 16, and 32. * **Y-axis:** Latency (ms), with a scale from 0.0 to 50.0, marked at intervals of 12.5 (0.0, 12.5, 25.0, 37.5, 50.0). * **Legend:** Located in the top-left corner. * FP16: Represented by light gray bars. * INT8: Represented by dark red bars. ### Detailed Analysis The chart presents latency values for FP16 (light gray) and INT8 (dark red) at batch sizes of 1, 8, 16, and 32. * **Batch Size 1:** * FP16: Latency is 2.24 ms. * INT8: Latency is 2.26 ms. * **Batch Size 8:** * FP16: Latency is 11.14 ms. * INT8: Latency is 7.93 ms. * **Batch Size 16:** * FP16: Latency is 21.5 ms. * INT8: Latency is 14.66 ms. * **Batch Size 32:** * FP16: Latency is 43.81 ms. * INT8: Latency is 29.07 ms. ### Key Observations * Latency increases as batch size increases for both FP16 and INT8. * INT8 consistently shows lower latency than FP16 for all batch sizes. * The difference in latency between FP16 and INT8 becomes more pronounced as the batch size increases. ### Interpretation The data suggests that using INT8 data type results in lower latency compared to FP16, especially at larger batch sizes. This indicates that INT8 is more efficient for processing larger batches of data, potentially due to reduced memory bandwidth requirements and faster computation. The increasing latency with batch size is expected, as larger batches require more processing time. The significant difference in latency at batch size 32 suggests that INT8 could offer substantial performance benefits in scenarios involving large batch processing.

## Bar Chart: Latency vs. Batch Size for FP16 and INT8 ### Overview This bar chart displays the latency in milliseconds (ms) for two different data types, FP16 and INT8, across various batch sizes. The x-axis represents the batch size, and the y-axis represents the latency. For each batch size, there are two bars: one for FP16 (light gray) and one for INT8 (dark red). ### Components/Axes * **Y-axis Title**: "Latency(ms)" * **Scale**: Linear, ranging from 0.0 to 50.0. * **Tick Marks**: 0.0, 12.5, 25.0, 37.5, 50.0. * **X-axis Title**: "Batch Size" * **Categories**: 1, 8, 16, 32. * **Legend**: Located in the top-left quadrant of the chart. * **FP16**: Represented by a light gray rectangle. * **INT8**: Represented by a dark red rectangle. ### Detailed Analysis The chart presents latency values for batch sizes of 1, 8, 16, and 32. **Batch Size 1:** * **FP16**: The light gray bar reaches a height of approximately 2.24 ms. * **INT8**: The dark red bar reaches a height of approximately 2.26 ms. **Batch Size 8:** * **FP16**: The light gray bar reaches a height of approximately 11.14 ms. * **INT8**: The dark red bar reaches a height of approximately 7.93 ms. **Batch Size 16:** * **FP16**: The light gray bar reaches a height of approximately 21.5 ms. * **INT8**: The dark red bar reaches a height of approximately 14.66 ms. **Batch Size 32:** * **FP16**: The light gray bar reaches a height of approximately 43.81 ms. * **INT8**: The dark red bar reaches a height of approximately 29.07 ms. ### Key Observations * **General Trend**: For both FP16 and INT8, latency generally increases as the batch size increases. * **FP16 Trend**: The latency for FP16 shows a significant upward trend, with a substantial jump from batch size 16 to 32. * **INT8 Trend**: The latency for INT8 also increases with batch size, but at a less dramatic rate compared to FP16, especially at larger batch sizes. * **Comparison**: At batch size 1, the latencies are very similar. However, as batch size increases, FP16 consistently shows higher latency than INT8, with the difference becoming more pronounced at batch sizes 16 and 32. ### Interpretation This chart demonstrates the impact of batch size on latency for different data precisions (FP16 and INT8). The data suggests that while increasing batch size generally leads to higher latency for both precisions, INT8 exhibits better scalability and lower latency at larger batch sizes compared to FP16. This implies that for applications where latency is a critical factor and large batch sizes are utilized, INT8 might be a more performant choice. The significant increase in latency for FP16 at batch size 32 could indicate a bottleneck or a point where the computational overhead of FP16 becomes more dominant.

\n ## Bar Chart: Latency vs. Batch Size for FP16 and INT8 ### Overview This bar chart compares the latency (in milliseconds) of operations performed using FP16 and INT8 data types across different batch sizes. The x-axis represents the batch size, and the y-axis represents the latency. The chart displays latency values for each batch size and data type combination. ### Components/Axes * **X-axis:** Batch Size (with markers at 1, 8, 16, and 32) * **Y-axis:** Latency (ms), ranging from 0.0 to 50.0 * **Legend:** * FP16 (represented by light gray bars) * INT8 (represented by dark red bars) * **Data Series:** Two data series, one for FP16 and one for INT8. ### Detailed Analysis The chart presents latency values for four batch sizes (1, 8, 16, and 32) and two data types (FP16 and INT8). * **Batch Size 1:** * FP16: 2.24 ms * INT8: 2.26 ms * **Batch Size 8:** * FP16: 11.14 ms * INT8: 7.93 ms * **Batch Size 16:** * FP16: 21.5 ms * INT8: 14.66 ms * **Batch Size 32:** * FP16: 43.81 ms * INT8: 29.07 ms **Trends:** * **FP16:** The FP16 latency increases almost linearly with batch size. The line slopes upward. * **INT8:** The INT8 latency also increases with batch size, but at a slower rate than FP16. The line slopes upward, but less steeply. ### Key Observations * INT8 consistently exhibits lower latency than FP16 across all batch sizes. * The difference in latency between FP16 and INT8 becomes more pronounced as the batch size increases. * The latency increases significantly as the batch size increases for both data types. ### Interpretation The data suggests that using INT8 quantization can significantly reduce latency compared to FP16, especially when processing larger batches of data. This is likely due to the reduced memory footprint and computational complexity associated with INT8 operations. The linear increase in latency with batch size indicates that the processing time scales with the amount of data being processed. The widening gap between FP16 and INT8 latency as batch size increases suggests that the benefits of INT8 quantization become more substantial when dealing with larger workloads. This chart demonstrates a clear trade-off between precision (FP16) and performance (INT8). The choice between the two would depend on the specific application's requirements for accuracy and speed.

\n ## Bar Chart: Latency vs. Batch Size for FP16 and INT8 Precision ### Overview The image is a grouped bar chart comparing the inference latency (in milliseconds) of two numerical precision formats, FP16 (16-bit floating point) and INT8 (8-bit integer), across four different batch sizes. The chart demonstrates how latency scales with increasing batch size for each format and highlights the performance difference between them. ### Components/Axes * **Chart Type:** Grouped vertical bar chart. * **X-Axis:** Labeled **"Batch Size"**. It has four discrete categories: **1, 8, 16, and 32**. * **Y-Axis:** Labeled **"Latency(ms)"**. The scale is linear, ranging from **0.0 to 50.0**, with major tick marks at intervals of 12.5 (0.0, 12.5, 25.0, 37.5, 50.0). * **Legend:** Located in the **top-left corner** of the chart area. * A light gray rectangle corresponds to **"FP16"**. * A dark red (maroon) rectangle corresponds to **"INT8"**. * **Data Labels:** The exact latency value is printed above each bar. ### Detailed Analysis The chart presents the following precise data points, confirmed by matching bar color to the legend: | Batch Size | FP16 Latency (ms) | INT8 Latency (ms) | | :--- | :--- | :--- | | **1** | 2.24 | 2.26 | | **8** | 11.14 | 7.93 | | **16** | 21.5 | 14.66 | | **32** | 43.81 | 29.07 | **Trend Verification:** * **FP16 Series (Light Gray Bars):** The latency shows a clear, near-linear upward trend as batch size increases. The line formed by the tops of the gray bars slopes steeply upward from left to right. * **INT8 Series (Dark Red Bars):** The latency also increases with batch size, but the slope is less steep than for FP16. The upward trend is consistent. ### Key Observations 1. **Performance Crossover:** At the smallest batch size of 1, the latencies are virtually identical (2.24 ms vs. 2.26 ms), with INT8 being marginally slower. This is the only point where INT8 does not show an advantage. 2. **Growing Advantage with Batch Size:** For all batch sizes greater than 1, INT8 demonstrates significantly lower latency than FP16. The absolute and relative performance gap widens as the batch size increases. 3. **Scaling Behavior:** Both precision formats exhibit latency that scales roughly linearly with batch size. However, the scaling factor (the slope) is lower for INT8. 4. **Magnitude of Difference:** At the largest measured batch size (32), INT8 latency (29.07 ms) is approximately **33.6% lower** than FP16 latency (43.81 ms). ### Interpretation This chart provides a clear performance comparison relevant to machine learning inference optimization. The data suggests that: * **INT8 quantization offers a substantial latency benefit** over FP16 for batched inference workloads. This benefit becomes more pronounced as the batch size increases, making INT8 particularly advantageous for high-throughput scenarios. * The minimal difference at batch size 1 indicates that the overhead of processing in INT8 format is negligible compared to the computational savings it provides. The primary advantage stems from more efficient computation and memory bandwidth usage with 8-bit integers versus 16-bit floats. * The consistent linear scaling for both formats implies predictable performance characteristics, which is crucial for system design and capacity planning. The lower slope for INT8 means it can handle larger batches with a smaller relative penalty in latency. * **Practical Implication:** For applications where maximizing throughput (processed items per second) is critical, using INT8 precision is strongly favored, especially when operating with batch sizes of 8 or more. The choice between FP16 and INT8 at very low batch sizes (like 1) may depend on other factors such as model accuracy requirements, as the latency difference is negligible.

## Bar Chart: Latency Comparison Between FP16 and INT8 Across Batch Sizes ### Overview The chart compares latency (in milliseconds) for two computational methods, **FP16** (gray bars) and **INT8** (red bars), across four batch sizes: 1, 8, 16, and 32. Latency increases with batch size for both methods, but FP16 consistently exhibits higher latency than INT8. ### Components/Axes - **X-axis (Batch Size)**: Labeled "Batch Size" with discrete categories: 1, 8, 16, 32. - **Y-axis (Latency)**: Labeled "Latency (ms)" with a scale from 0.0 to 50.0. - **Legend**: Located in the top-left corner, associating gray with FP16 and red with INT8. - **Bar Groups**: Each batch size has two adjacent bars (FP16 and INT8) with numerical values labeled on top. ### Detailed Analysis - **Batch Size 1**: - FP16: 2.24 ms - INT8: 2.26 ms - **Batch Size 8**: - FP16: 11.14 ms - INT8: 7.93 ms - **Batch Size 16**: - FP16: 21.5 ms - INT8: 14.66 ms - **Batch Size 32**: - FP16: 43.81 ms - INT8: 29.07 ms ### Key Observations 1. **FP16 vs. INT8**: FP16 latency is consistently higher than INT8 across all batch sizes. 2. **Scaling Behavior**: Both methods show linear latency growth with batch size, but FP16 scales more steeply. 3. **Divergence at Larger Batches**: The latency gap widens significantly at batch size 32 (FP16: 43.81 ms vs. INT8: 29.07 ms). ### Interpretation The data demonstrates that **INT8 is more latency-efficient than FP16**, particularly at larger batch sizes. This suggests INT8’s use of lower-precision arithmetic reduces computational overhead, making it preferable for high-throughput applications. However, FP16’s higher precision may be necessary for tasks requiring numerical accuracy, despite its performance cost. The widening gap at batch size 32 highlights a critical trade-off between precision and efficiency in system design.