## Heatmap: Streaming LLM Performance by Context Length ### Overview The image is a heatmap visualizing the performance of streaming and non-streaming large language models (LLMs) across varying context lengths. The chart uses color gradients to represent performance metrics, with red/orange indicating streaming and green indicating non-streaming. Context lengths range from 10,000 to 125,000 tokens, and performance is plotted along the y-axis. ### Components/Axes - **X-Axis (Context Length)**: Labeled "Context Length" with values from 10,000 to 125,000 tokens in increments of 10,000. - **Y-Axis (Performance)**: Labeled "Streaming" (top) and "Non-Streaming" (bottom), with no numerical scale but categorical separation. - **Legend**: Located on the right, with red representing "Streaming" and green representing "Non-Streaming." ### Detailed Analysis - **Streaming (Red/Orange)**: - Performance varies significantly across context lengths. - Darker red (higher performance) dominates shorter contexts (10k–50k tokens). - Gradual shift to orange (lower performance) as context length increases (50k–125k tokens). - Notable gaps (lighter orange) at 70k, 90k, and 110k tokens, suggesting performance drops at these ranges. - **Non-Streaming (Green)**: - Uniformly green across most context lengths, indicating stable performance. - Darker green squares at 20k, 30k, 80k, 90k, 100k, and 110k tokens, possibly denoting optimal performance in these ranges. - No significant degradation observed across context lengths. ### Key Observations 1. **Streaming Degradation**: Streaming performance declines as context length increases, with the most pronounced drops at 70k, 90k, and 110k tokens. 2. **Non-Streaming Stability**: Non-streaming maintains consistent performance, with minor optimizations at specific context lengths. 3. **Color Consistency**: All red/orange regions align with the "Streaming" legend, and green regions match "Non-Streaming," confirming accurate color coding. ### Interpretation The heatmap suggests that streaming LLMs face scalability challenges with longer contexts, likely due to incremental processing overhead. Non-streaming models, while less flexible, handle large contexts more efficiently. The performance gaps in streaming at specific context lengths (e.g., 70k, 90k) may indicate architectural limitations or resource bottlenecks. This data highlights a trade-off between real-time adaptability (streaming) and computational efficiency (non-streaming) in LLM design.