# Technical Data Extraction: Cache Hit Rate Comparison
## 1. Image Overview
This image is a grouped bar chart comparing the performance of **SGLang** against an **Optimal** baseline across eleven different Large Language Model (LLM) benchmarks and tasks. The metric measured is the **Cache Hit Rate (%)**.
## 2. Chart Metadata
* **Y-Axis Title:** Cache Hit Rate (%)
* **Y-Axis Scale:** 0.0 to 100.0 (increments of 20.0)
* **X-Axis Categories:** 11 distinct LLM tasks/benchmarks.
* **Legend (Top Center):**
* **Orange Bar:** Achieved cache hit rate with SGLang
* **Light Blue Bar:** Optimal cache hit rate
## 3. Data Table Extraction
The following table reconstructs the visual data points. Values are estimated based on the Y-axis markers.
| Task / Benchmark | Achieved (SGLang - Orange) | Optimal (Light Blue) | Gap Analysis |
| :--- | :---: | :---: | :--- |
| **MMLU** | ~85% | ~85% | Identical |
| **ReAct Agents** | ~94% | ~94% | Identical |
| **Generative Agents** | ~91% | ~91% | Identical |
| **Tree of Thought** | ~98% | ~99% | Negligible gap |
| **Skeleton of Thought** | ~92% | ~95% | Small gap |
| **LLM Judge** | ~72% | ~73% | Negligible gap |
| **HellaSwag** | ~98% | ~98% | Identical |
| **JSON Decoding** | ~88% | ~88% | Identical |
| **Multi-Turn Chat (short)** | ~50% | ~60% | Moderate gap |
| **Multi-Turn Chat (long)** | ~57% | ~74% | Significant gap |
| **DSPy RAG Pipeline** | ~90% | ~93% | Small gap |
## 4. Component Analysis and Trends
### Header/Legend Region
The legend is positioned at the top center. It clearly distinguishes between the practical implementation (SGLang) and the theoretical maximum (Optimal).
### Main Chart Region (Trends)
* **High Performance Consistency:** In 7 out of 11 tasks (MMLU, ReAct Agents, Generative Agents, Tree of Thought, HellaSwag, JSON Decoding, and DSPy RAG), SGLang achieves a cache hit rate that is either identical or nearly identical to the optimal rate, typically exceeding 85%.
* **Complexity Sensitivity:** The performance gap between SGLang and the Optimal rate widens significantly in "Multi-Turn Chat" scenarios.
* In **Multi-Turn Chat (short)**, there is a visible ~10% discrepancy.
* In **Multi-Turn Chat (long)**, the discrepancy is at its largest (approx. 17%), indicating that longer conversational contexts are more challenging for the current SGLang caching implementation to optimize fully.
* **Lowest Overall Hit Rate:** The "Multi-Turn Chat (short)" task shows the lowest achieved hit rate for SGLang at approximately 50%.
* **Highest Overall Hit Rate:** "Tree of Thought" and "HellaSwag" show the highest efficiency, nearing 100% cache hit rates.
### Footer/X-Axis Region
The labels are clearly legible and categorized by task type, ranging from standard benchmarks (MMLU, HellaSwag) to specific architectural patterns (ReAct, Tree of Thought, RAG).
