## Line Chart: Qwen3-4B Performance Comparison
### Overview
The chart compares the performance of three quantization methods (BF16, INT4, INT4+BDR) for the Qwen3-4B model, plotting **TPS_req (tokens/second)** against **Per-GPU throughput (TPS_sys/N_GPU, tok/s/GPU)**. All three lines show a downward trend, indicating decreasing TPS_req as throughput increases.
### Components/Axes
- **X-axis**: Per-GPU throughput (TPS_sys/N_GPU, tok/s/GPU)
- Markers: 0, 200, 400, 600, 800, 1000, 1200, 1400, 1600
- **Y-axis**: TPS_req (tok/s)
- Markers: 25, 50, 75, 100, 125, 150, 175
- **Legend**:
- **BF16** (blue line with circle markers)
- **INT4** (orange line with circle markers)
- **INT4+BDR** (green line with circle markers)
- Positioned in the bottom-left corner.
### Detailed Analysis
1. **BF16 (Blue Line)**
- Starts at **150 tok/s** at x=0.
- Gradual decline to **140 tok/s** at x=400.
- Further drops to **130 tok/s** at x=800.
- Sharp decline to **70 tok/s** at x=1000.
- Final drop to **30 tok/s** at x=1400.
2. **INT4 (Orange Line)**
- Starts at **180 tok/s** at x=0.
- Declines to **160 tok/s** at x=400.
- Further drops to **140 tok/s** at x=800.
- Sharp decline to **130 tok/s** at x=1000.
- Final drop to **85 tok/s** at x=1200.
- Ends at **15 tok/s** at x=1600.
3. **INT4+BDR (Green Line)**
- Starts at **180 tok/s** at x=0.
- Declines to **165 tok/s** at x=400.
- Further drops to **145 tok/s** at x=800.
- Sharp decline to **135 tok/s** at x=1000.
- Further drops to **90 tok/s** at x=1200.
- Ends at **15 tok/s** at x=1600.
### Key Observations
- **INT4+BDR** consistently outperforms **INT4** and **BF16** across all throughput ranges, maintaining lower TPS_req.
- **BF16** shows the steepest decline after x=1000, suggesting reduced efficiency at higher throughputs.
- All methods converge to **~15 tok/s** at x=1600, indicating a performance floor.
- **INT4+BDR** retains ~10-15 tok/s advantage over **INT4** and ~30-40 tok/s advantage over **BF16** at mid-range throughputs (x=400–1000).
### Interpretation
The chart demonstrates that **INT4+BDR** offers the best trade-off between throughput and token processing efficiency, likely due to optimized quantization techniques. **BF16**, while simpler, becomes less efficient at higher throughputs, possibly due to higher memory or computational overhead. The convergence at x=1600 suggests diminishing returns for all methods at extreme throughputs. This data could guide model deployment decisions, favoring INT4+BDR for latency-sensitive applications.
