## Line Chart: Different Quantization Strategies on LongBench Accuracy vs. Cache Budget
### Overview
The chart compares the performance of four quantization strategies (FP16, INT8, INT4, INT2) on the LongBench benchmark across varying cache budgets (20%, 40%, 60%, 80%). Accuracy is measured on the y-axis (29–36), while cache budget is represented on the x-axis. Each strategy is visualized with a distinct line and marker type.
### Components/Axes
- **X-Axis (Cache Budget)**: Labeled "Cache Budget" with ticks at 20%, 40%, 60%, and 80%.
- **Y-Axis (LongBench Accuracy)**: Labeled "LongBench Accuracy" with increments from 29 to 36.
- **Legend**: Positioned in the top-right corner, mapping:
- **FP16**: Blue circles (●)
- **INT8**: Green squares (■)
- **INT4**: Red diamonds (◆)
- **INT2**: Yellow triangles (▲)
### Detailed Analysis
1. **FP16 (Blue Circles)**:
- **Trend**: Steady upward slope from 33.6 (20%) to 35.8 (80%).
- **Data Points**:
- 20%: 33.6
- 40%: 34.5
- 60%: 35.2
- 80%: 35.8
2. **INT8 (Green Squares)**:
- **Trend**: Gradual increase from 33.5 (20%) to 35.7 (80%).
- **Data Points**:
- 20%: 33.5
- 40%: 34.4
- 60%: 35.0
- 80%: 35.7
3. **INT4 (Red Diamonds)**:
- **Trend**: Slight upward trajectory with minimal fluctuation (33.8 to 35.5).
- **Data Points**:
- 20%: 33.8
- 40%: 34.6
- 60%: 34.8
- 80%: 35.5
4. **INT2 (Yellow Triangles)**:
- **Trend**: Slow, linear growth from 28.5 (20%) to 30.3 (80%).
- **Data Points**:
- 20%: 28.5
- 40%: 29.4
- 60%: 29.4
- 80%: 30.3
### Key Observations
- **FP16 and INT8** consistently outperform other strategies, with FP16 achieving the highest accuracy at 80% cache budget (35.8).
- **INT4** shows moderate performance, trailing FP16/INT8 but surpassing INT2.
- **INT2** lags significantly, with the lowest accuracy across all cache budgets and the flattest growth curve.
- All strategies improve with increased cache budget, but FP16 and INT8 demonstrate the strongest scalability.
### Interpretation
The data suggests that **FP16** and **INT8** are the most effective quantization strategies for maximizing LongBench accuracy, particularly at higher cache budgets. Their steep upward trends indicate efficient utilization of additional cache resources. In contrast, **INT2** underperforms, implying suboptimal cache efficiency or algorithmic limitations. **INT4** offers a middle ground, balancing accuracy and resource usage. These results highlight trade-offs between quantization precision (e.g., FP16’s higher precision vs. INT2’s lower precision) and computational efficiency, guiding choices based on application-specific priorities (e.g., accuracy vs. latency).
