## [Chart Type: Multi-Panel Performance Analysis (Line Graph + Bar Graphs)]
### Overview
The image contains three panels: a left line graph (Throughput vs. Latency) and two right bar graphs (Perplexity (PPL) and Accuracy (Acc) vs. Model Size). It compares the performance of different methods (Original, FLAP, LLM-Prn., Ours, SLEB, Ours-CPT) across metrics like throughput, latency, perplexity, and accuracy.
### Components/Axes
#### Left Panel: Throughput vs. Latency (Line Graph)
- **Y-axis**: Throughput (tokens/s) (logarithmic scale: 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192).
- **X-axis**: Latency (s) (linear scale: 1.6, 2.8, 4, 5.2, 6.4).
- **Legend**:
- Gray cross: *Original*
- Green circle: *FLAP*
- Green triangle: *LLM-Prn.*
- Blue square: *Ours*
- **Batch Sizes (M)**: Labeled on the *Ours* line: M1, M8, M16, M32, M64, M128, M256.
- **Setup**: 4.9B Parameters, 12 Input Tokens, 128 Output Tokens (text at bottom).
#### Right Top Panel: Perplexity (PPL) vs. Model Size (Bar Graph)
- **Y-axis**: PPL (lower = better; values: 20, 30, 40; >40 omitted).
- **X-axis**: Model Size (# Parameters: 5.5B, 3.7B, 2.7B).
- **Legend (top-right)**:
- Dark green: *FLAP (W%)*
- Light green: *LLM-Prn. (W%)*
- Light blue: *SLEB (D%)*
- Dark blue: *Ours-CPT (D%)*
#### Right Bottom Panel: Accuracy (Acc) vs. Model Size (Bar Graph)
- **Y-axis**: Accuracy (%) (values: 40, 50, 60).
- **X-axis**: Model Size (# Parameters: 5.5B, 3.7B, 2.7B).
- **Legend (top-right)**: Same as PPL (FLAP, LLM-Prn., SLEB, Ours-CPT).
### Detailed Analysis
#### Left Panel: Throughput vs. Latency
- **Trends**:
- *Ours* (blue squares) has the **highest throughput** across most latencies, especially at larger batch sizes (M128, M256).
- *Original* (gray crosses) has lower throughput, increasing with latency but slower than *Ours*.
- *FLAP* (green circles) and *LLM-Prn.* (green triangles) lie between *Original* and *Ours*, with *LLM-Prn.* slightly outperforming *FLAP* at some points.
- **Key Data Points** (approximate):
- *Ours* at M256: Throughput ~8192 tokens/s, Latency ~6.4s.
- *Original* at M256: Throughput ~4096 tokens/s, Latency ~6.4s.
#### Right Top Panel: Perplexity (PPL)
- **Trends**:
- *Ours-CPT* (dark blue) has the **lowest PPL** (best language modeling) across all model sizes.
- *FLAP*, *LLM-Prn.*, and *SLEB* have higher PPL (worse performance), especially at 3.7B and 2.7B (exceeding 40 for some).
- **Key Data Points** (approximate):
- 5.5B: *Ours-CPT* ~15, *FLAP* ~22, *LLM-Prn.* ~20, *SLEB* ~25.
- 3.7B: *Ours-CPT* ~15, *FLAP* ~40, *LLM-Prn.* ~40, *SLEB* ~40.
#### Right Bottom Panel: Accuracy (Acc)
- **Trends**:
- *Ours-CPT* (dark blue) has the **highest accuracy** across all model sizes.
- *SLEB* (light blue) has the lowest accuracy. *FLAP* and *LLM-Prn.* lie in between, with *LLM-Prn.* slightly outperforming *FLAP* at 3.7B and 2.7B.
- **Key Data Points** (approximate):
- 5.5B: *Ours-CPT* ~61%, *FLAP* ~61%, *LLM-Prn.* ~60%, *SLEB* ~55%.
- 3.7B: *Ours-CPT* ~57%, *FLAP* ~47%, *LLM-Prn.* ~50%, *SLEB* ~40%.
### Key Observations
- **Efficiency (Throughput/Latency)**: *Ours* outperforms *Original*, *FLAP*, and *LLM-Prn.* in throughput (tokens/s) at similar or lower latency, especially with larger batch sizes.
- **Performance (PPL/Acc)**: *Ours-CPT* outperforms *FLAP*, *LLM-Prn.*, and *SLEB* in both perplexity (lower = better) and accuracy (higher = better) across all model sizes.
- **Model Size Impact**: Larger models (5.5B) generally have better PPL and accuracy than smaller ones (3.7B, 2.7B), but *Ours-CPT* maintains strong performance even at 2.7B.
### Interpretation
The data suggests that the “Ours” method (and its variant “Ours-CPT”) is more efficient (higher throughput, lower latency) and effective (lower PPL, higher accuracy) than competing methods (Original, FLAP, LLM-Prn., SLEB). This implies:
- **Efficiency**: “Ours” processes more tokens per second with less delay, making it suitable for high-throughput applications.
- **Effectiveness**: “Ours-CPT” achieves better language modeling (lower PPL) and task accuracy, indicating improved model quality.
- **Scalability**: Performance gains hold across different model sizes (5.5B, 3.7B, 2.7B), suggesting the method scales well.
These results highlight the superiority of “Ours” in both efficiency and performance, making it a promising approach for large language model optimization.
