## Chart Type: Line Graphs Comparing Latency and TPOT ### Overview The image contains two line graphs comparing the performance of three different models (MLA, GDN-H, and Kimi Linear) in terms of latency and TPOT (likely Throughput Over Time). The left graph (a) shows latency (in seconds) as a function of prefilling length, while the right graph (b) shows TPOT (in milliseconds) as a function of decoding length. Both graphs use a logarithmic scale on the x-axis. ### Components/Axes **Left Graph (a):** * **Y-axis:** Latency (s), ranging from 0 to 60 seconds. * **X-axis:** Prefilling Length, with values 4K, 128K, 256K, 512K, and 1M. * **Legend (top-left):** * MLA: Dashed teal line with circular markers. * GDN-H: Solid orange line with circular markers. * Kimi Linear: Solid purple line with circular markers. **Right Graph (b):** * **Y-axis:** TPOT (ms), ranging from 5 to 15 milliseconds. * **X-axis:** Decoding Length, with values 4K, 128K, 256K, 512K, and 1M. * **Legend (top-left):** * MLA: Dashed teal line with circular markers. * GDN-H: Solid orange line with circular markers. * Kimi Linear: Solid purple line with circular markers. ### Detailed Analysis **Left Graph (a) - Latency vs. Prefilling Length:** * **MLA (Dashed Teal):** Latency remains near 0 until 128K, then increases sharply. * 4K: ~0s * 128K: ~1s * 256K: ~3s * 512K: ~10s * 1M: ~30s * **GDN-H (Solid Orange):** GDN-H is not visible on the graph, suggesting it has very high latency values. * **Kimi Linear (Solid Purple):** Latency remains near 0 until 256K, then increases. * 4K: ~0s * 128K: ~0s * 256K: ~0.5s * 512K: ~4s * 1M: ~10s * **Annotations:** * A red double-arrow indicates the difference between MLA and Kimi Linear at 512K, labeled "2.3x". * A red double-arrow indicates the difference between MLA and Kimi Linear at 1M, labeled "2.9x". **Right Graph (b) - TPOT vs. Decoding Length:** * **MLA (Dashed Teal):** TPOT increases gradually with decoding length. * 4K: ~5ms * 128K: ~6ms * 256K: ~7ms * 512K: ~9ms * 1M: ~11ms * **GDN-H (Solid Orange):** GDN-H is not visible on the graph, suggesting it has very low TPOT values. * **Kimi Linear (Solid Purple):** TPOT increases gradually with decoding length. * 4K: ~5ms * 128K: ~5ms * 256K: ~5.5ms * 512K: ~6.5ms * 1M: ~8ms * **Annotations:** * A red double-arrow indicates the difference between MLA and Kimi Linear at 512K, labeled "1.8x". * A red double-arrow indicates the difference between MLA and Kimi Linear at 1M, labeled "2.2x". ### Key Observations * In the Latency graph, MLA's latency increases more rapidly than Kimi Linear's as prefilling length increases. * In the TPOT graph, MLA's TPOT is consistently higher than Kimi Linear's as decoding length increases. * GDN-H is not visible on either graph, suggesting it has very poor performance in both latency and TPOT. * The annotations on both graphs highlight the increasing performance gap between MLA and Kimi Linear at higher lengths. ### Interpretation The data suggests that MLA generally outperforms Kimi Linear in both latency and TPOT, especially at larger prefilling/decoding lengths. The annotations emphasize this performance gap. The absence of GDN-H from the graphs indicates that it is significantly less efficient than both MLA and Kimi Linear, making it an unsuitable choice for these tasks. The logarithmic scale on the x-axis suggests that the performance differences become more pronounced as the input length increases. The "x" values on the red arrows indicate a multiplicative factor, showing how much larger the MLA value is compared to the Kimi Linear value at those specific points.