# Technical Document Analysis: Throughput Comparison of SGLang and vLLM
## 1. Chart Type and Structure
- **Chart Type**: Grouped bar chart comparing normalized throughput of two systems (SGLang and vLLM) across 11 categories.
- **Axes**:
- **X-axis**: Categories (11 total), labeled as:
- MMLU
- ReAct Agents
- Generative Agents
- Tree of Thought
- Skeleton of Thought
- LLM Judge
- HellaSwag
- JSON Decoding
- Multi-Turn Chat (short)
- Multi-Turn Chat (long)
- DSPy RAG Pipeline
- **Y-axis**: "Throughput (Normalized)" with values ranging from 0.0 to 1.0 in increments of 0.2.
## 2. Legend
- **Position**: Top-right corner of the chart.
- **Labels**:
- **Orange**: SGLang
- **Green**: vLLM
## 3. Key Trends and Data Points
### SGLang (Orange Bars)
- **Consistent Performance**: SGLang outperforms vLLM in all 11 categories.
- **Highest Throughput**:
- **Generative Agents**: ~0.75
- **Skeleton of Thought**: ~0.75
- **Lowest Throughput**:
- **MMLU**: ~1.0 (saturated at maximum y-axis value)
- **ReAct Agents**: ~1.0 (saturated at maximum y-axis value)
### vLLM (Green Bars)
- **Variable Performance**: Significantly lower throughput than SGLang in most categories.
- **Highest Throughput**:
- **Multi-Turn Chat (long)**: ~0.6
- **Lowest Throughput**:
- **HellaSwag**: ~0.02 (near baseline)
### Cross-Category Comparison
- **Notable Disparities**:
- **Generative Agents**: SGLang (~0.75) vs. vLLM (~0.25) → 3x difference.
- **Skeleton of Thought**: SGLang (~0.75) vs. vLLM (~0.25) → 3x difference.
- **Multi-Turn Chat (long)**: SGLang (~1.0) vs. vLLM (~0.6) → 1.6x difference.
## 4. Verification and Validation
### Trend Verification
- **SGLang**: All bars slope upward relative to vLLM, confirming consistent superiority.
- **vLLM**: Bars show minimal height across most categories, with exceptions in chat-based tasks.
### Color Consistency Check
- All orange bars correspond to SGLang (legend).
- All green bars correspond to vLLM (legend).
## 5. Spatial Grounding
- **Legend Position**: Top-right (coordinates not explicitly defined but visually anchored).
- **Bar Alignment**: Each category has two bars (orange/green) aligned vertically.
## 6. Component Isolation
- **Header**: Chart title and legend.
- **Main Chart**: 11 grouped bars with y-axis scaling.
- **Footer**: No additional text or annotations.
## 7. Missing Data and Limitations
- **Exact Numerical Values**: Not provided in the image; approximations based on bar height relative to y-axis.
- **Error Bars/Confidence Intervals**: Absent, limiting statistical interpretation.
## 8. Conclusion
SGLang demonstrates consistently higher normalized throughput than vLLM across all evaluated categories, with the largest performance gaps in generative and reasoning tasks (e.g., Generative Agents, Skeleton of Thought). Chat-based tasks (Multi-Turn Chat) show the smallest disparity, suggesting vLLM retains partial utility in conversational contexts.
