## 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 for different batch sizes (1, 8, 16, and 32). The chart shows how latency increases with batch size for both data types, but INT8 generally has lower latency than FP16 for the same batch size. ### Components/Axes * **X-axis:** Batch Size, with values 1, 8, 16, and 32. * **Y-axis:** Latency (ms), ranging from 0.0 to 30.0, with tick marks at 0.0, 7.5, 15.0, 22.5, and 30.0. * **Legend:** Located in the top-left corner. * Gray bar: FP16 * Dark Red bar: INT8 ### Detailed Analysis Here's a breakdown of the latency values for each batch size and data type: * **Batch Size 1:** * FP16 (Gray): 2.97 ms * INT8 (Dark Red): 2.91 ms * **Batch Size 8:** * FP16 (Gray): 8.09 ms * INT8 (Dark Red): 5.44 ms * **Batch Size 16:** * FP16 (Gray): 15.03 ms * INT8 (Dark Red): 9.23 ms * **Batch Size 32:** * FP16 (Gray): 29.66 ms * INT8 (Dark Red): 17.28 ms **Trend Verification:** Both FP16 and INT8 latencies increase as the batch size increases. ### Key Observations * For all batch sizes, INT8 has lower latency than FP16. * The difference in latency between FP16 and INT8 becomes more pronounced as the batch size increases. * The latency increases approximately linearly with batch size for both data types, but the slope is steeper for FP16. ### Interpretation The chart demonstrates that using INT8 data type results in lower latency compared to FP16 for the given task, and this advantage becomes more significant as the batch size increases. This suggests that INT8 is more efficient for larger batch sizes in this particular scenario. The near-linear increase in latency with batch size indicates a consistent processing overhead for each additional element in the batch. The steeper slope for FP16 suggests that it is more sensitive to increases in batch size than INT8.

## Bar Chart: Latency vs. Batch Size for FP16 and INT8 ### Overview This image is a bar chart comparing the latency (in milliseconds) for two different data types, FP16 and INT8, across various batch sizes. The chart displays four sets of paired bars, each representing a specific batch size: 1, 8, 16, and 32. ### Components/Axes * **Y-axis Title**: "Latency(ms)" * **Scale**: Linear, ranging from 0.0 to 30.0, with major tick marks at 0.0, 7.5, 15.0, 22.5, and 30.0. * **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 maroon rectangle. ### Detailed Analysis The chart presents latency values for FP16 and INT8 at batch sizes of 1, 8, 16, and 32. **Batch Size 1:** * **FP16**: The light gray bar reaches a height of approximately 2.97 ms. * **INT8**: The dark maroon bar reaches a height of approximately 2.91 ms. **Batch Size 8:** * **FP16**: The light gray bar reaches a height of approximately 8.09 ms. * **INT8**: The dark maroon bar reaches a height of approximately 5.44 ms. **Batch Size 16:** * **FP16**: The light gray bar reaches a height of approximately 15.03 ms. * **INT8**: The dark maroon bar reaches a height of approximately 9.23 ms. **Batch Size 32:** * **FP16**: The light gray bar reaches a height of approximately 29.66 ms. * **INT8**: The dark maroon bar reaches a height of approximately 17.28 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, accelerating with larger batch sizes. * **INT8 Trend**: The latency for INT8 also increases with batch size, but at a slower rate compared to FP16, especially at larger batch sizes. * **Comparison**: At batch size 1, FP16 and INT8 have very similar latencies. However, as batch size increases, FP16 consistently exhibits higher latency than INT8. The difference in latency between FP16 and INT8 becomes more pronounced at batch sizes 16 and 32. ### Interpretation This chart demonstrates the performance characteristics of FP16 and INT8 data types in terms of latency as batch size varies. The data suggests that while both data types experience increased latency with larger batch sizes, INT8 offers a more favorable latency profile, particularly for larger batch sizes. This implies that INT8 might be a more efficient choice for applications requiring high throughput and low latency when dealing with substantial amounts of data. The increasing divergence in latency between FP16 and INT8 as batch size grows could be attributed to factors like memory bandwidth, computational efficiency, or specific hardware optimizations for integer operations. The initial similarity at batch size 1 might indicate that overheads dominate at very small batch sizes, masking the underlying performance differences.

\n ## Bar Chart: Latency vs. Batch Size for FP16 and INT8 ### Overview This bar chart compares the latency (in milliseconds) of two data types, FP16 and INT8, across different batch sizes (1, 8, 16, and 32). The chart uses a bar graph format to visually represent the latency for each combination of data type and batch size. ### Components/Axes * **X-axis:** Batch Size (with markers 1, 8, 16, 32) * **Y-axis:** Latency (ms) - Scale ranges from 0.0 to 30.0, with increments of 5.0. * **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 each batch size and data type. * **Batch Size 1:** * FP16: Approximately 2.97 ms. * INT8: Approximately 2.91 ms. * **Batch Size 8:** * FP16: Approximately 8.09 ms. * INT8: Approximately 5.44 ms. * **Batch Size 16:** * FP16: Approximately 15.03 ms. * INT8: Approximately 9.23 ms. * **Batch Size 32:** * FP16: Approximately 29.66 ms. * INT8: Approximately 17.28 ms. **Trends:** * **FP16:** The latency increases consistently as the batch size increases. The increase appears roughly linear. * **INT8:** The latency also increases with batch size, but the rate of increase appears slightly less steep than FP16. ### 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. At a batch size of 1, the difference is minimal (approximately 0.06 ms), but at a batch size of 32, the difference is significant (approximately 12.38 ms). * The latency values for both data types increase substantially as the batch size grows. ### 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 requirements of INT8. The consistent increase in latency with batch size for both data types is expected, as larger batches require more processing time. The widening gap between FP16 and INT8 latency as batch size increases indicates that the benefits of INT8 become more substantial with larger workloads. This information is valuable for optimizing performance in machine learning inference, where latency is a critical factor. The chart demonstrates a clear trade-off between precision (FP16) and speed (INT8).

## 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 visually demonstrates the performance advantage of INT8 quantization over FP16, particularly as the workload (batch size) increases. ### 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 30.0** with major tick marks at intervals of 7.5 (0.0, 7.5, 15.0, 22.5, 30.0). * **Legend:** Positioned in the **top-left corner** of the chart area. It contains two entries: * A light gray rectangle labeled **"FP16"**. * A dark red (maroon) rectangle labeled **"INT8"**. * **Data Series:** Two series of bars, grouped by batch size. * **FP16 Series:** Light gray bars. * **INT8 Series:** Dark red bars. ### Detailed Analysis The exact latency values are annotated above each bar. **Batch Size = 1:** * FP16 (light gray bar): **2.97 ms** * INT8 (dark red bar): **2.91 ms** * *Trend Check:* Both bars are very short and nearly equal in height, indicating minimal latency difference at the smallest batch size. **Batch Size = 8:** * FP16 (light gray bar): **8.09 ms** * INT8 (dark red bar): **5.44 ms** * *Trend Check:* Both bars are taller than at batch size 1. The FP16 bar is now noticeably taller than the INT8 bar. **Batch Size = 16:** * FP16 (light gray bar): **15.03 ms** * INT8 (dark red bar): **9.23 ms** * *Trend Check:* Latency continues to increase for both. The height difference between the FP16 and INT8 bars has grown larger. **Batch Size = 32:** * FP16 (light gray bar): **29.66 ms** * INT8 (dark red bar): **17.28 ms** * *Trend Check:* This is the tallest pair of bars. The FP16 bar is significantly taller than the INT8 bar, showing the largest absolute difference in latency. ### Key Observations 1. **Consistent Performance Advantage:** For every batch size tested, the INT8 (dark red) bar is shorter than the corresponding FP16 (light gray) bar, indicating lower latency. 2. **Diverging Trend:** The performance gap between FP16 and INT8 widens as the batch size increases. At batch size 1, the difference is negligible (0.06 ms). At batch size 32, the difference is substantial (12.38 ms). 3. **Scaling Behavior:** Latency for both precision formats increases with batch size. However, the rate of increase (slope) is steeper for FP16 than for INT8. 4. **Data Integrity:** All values are explicitly labeled, removing ambiguity in reading the bar heights against the y-axis scale. ### Interpretation This chart provides clear empirical evidence for the inference performance benefits of using INT8 quantization over FP16 precision in a computational workload (likely a neural network inference task). * **What the data suggests:** INT8 quantization reduces computational latency. This benefit becomes more pronounced under heavier loads (larger batch sizes), suggesting that INT8 operations scale more efficiently. The near-equal latency at batch size 1 indicates that the overhead of quantization/dequantization or other fixed costs may be minimal compared to the compute savings. * **How elements relate:** The x-axis (Batch Size) represents the independent variable—the workload intensity. The y-axis (Latency) is the dependent variable—the performance cost. The two bar colors (FP16 vs. INT8) represent the independent variable being tested—the precision format. The chart's structure directly compares the dependent variable across the two conditions for each level of workload. * **Notable implications:** For system designers and ML engineers, this data argues for adopting INT8 quantization to improve throughput and reduce response times in production inference systems, especially for services that process requests in batches. The significant saving at batch size 32 (a ~42% reduction in latency) could translate to major cost savings or increased capacity in a deployed service. The chart effectively communicates that the performance advantage of INT8 is not static but scales favorably with demand.

## 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 30.0 in increments of 7.5. - **Legend**: Located in the top-left corner, associating gray with FP16 and red with INT8. - **Bars**: Paired bars for each batch size, with numerical values labeled on top of each bar. ### Detailed Analysis - **Batch Size 1**: - FP16: 2.97 ms (gray bar). - INT8: 2.91 ms (red bar). - **Batch Size 8**: - FP16: 8.09 ms (gray bar). - INT8: 5.44 ms (red bar). - **Batch Size 16**: - FP16: 15.03 ms (gray bar). - INT8: 9.23 ms (red bar). - **Batch Size 32**: - FP16: 29.66 ms (gray bar). - INT8: 17.28 ms (red bar). ### Key Observations 1. **FP16 vs. INT8**: FP16 latency is consistently higher than INT8 across all batch sizes (e.g., 29.66 ms vs. 17.28 ms at batch size 32). 2. **Scaling Trends**: - FP16 latency increases non-linearly with batch size (e.g., 2.97 → 29.66 ms as batch size grows from 1 to 32). - INT8 latency increases linearly (e.g., 2.91 → 17.28 ms over the same range). 3. **Gap Widening**: The performance gap between FP16 and INT8 widens as batch size increases (e.g., 0.06 ms difference at batch size 1 vs. 12.38 ms at batch size 32). ### Interpretation - **Technical Implications**: FP16’s higher latency suggests it is less efficient for large-scale computations, likely due to the overhead of floating-point operations. INT8’s lower latency indicates better optimization for integer-based processing, which is common in machine learning inference. - **Batch Size Sensitivity**: The non-linear scaling of FP16 latency highlights its inefficiency for large batches, making INT8 a preferable choice for high-throughput applications. - **Outliers**: No anomalies detected; trends align with expectations for computational method performance.