## Line Chart: Average Accuracy vs Training Tokens (Quadrant a) ### Overview A line chart comparing the average accuracy of multiple models across different training token scales (10B to 50B). The chart shows six distinct data series with varying performance trajectories. ### Components/Axes - **X-axis**: Training Tokens (10B, 20B, 30B, 40B, 50B) - **Y-axis**: Avg. Accuracy (41% to 45%) - **Legend**: - Green: MLA-256 - Blue: EG-MLA-emb256 - Orange: EG-MLA-emb128 - Purple: EG-MLA-emb512 - Red: EG-MLA-emb1024 - Pink: EG-MLA-emb5048 ### Detailed Analysis 1. **MLA-256** (Green): Starts at ~42.2% at 10B, rises steadily to ~43.8% at 50B 2. **EG-MLA-emb256** (Blue): Begins at ~41.8% at 10B, plateaus at ~43.2% by 30B, then climbs to ~44.1% at 50B 3. **EG-MLA-emb128** (Orange): Starts at ~42.0% at 10B, follows a similar trajectory to EG-MLA-emb256 but with slightly lower gains 4. **EG-MLA-emb512** (Purple): Begins at ~42.5% at 10B, shows the steepest ascent to ~44.8% at 50B 5. **EG-MLA-emb1024** (Red): Starts at ~42.8% at 10B, maintains the highest accuracy throughout, reaching ~45.2% at 50B 6. **EG-MLA-emb5048** (Pink): Starts at ~42.3% at 10B, shows the most gradual improvement to ~45.5% at 50B ### Key Observations - All models show improved accuracy with increased training tokens - Larger embedding sizes (5048) achieve highest final accuracy but with slower improvement rate - MLA-256 (smallest embedding) shows most consistent gains - EG-MLA-emb1024 maintains lead throughout training progression ### Interpretation The chart demonstrates that larger embedding sizes generally yield better performance, but with diminishing returns. The MLA-256 model shows optimal efficiency, achieving ~43.8% accuracy at 50B tokens with minimal computational overhead. The EG-MLA-emb1024 model, while achieving highest accuracy, requires significantly more resources for marginal gains. --- ## Line Chart: Loss vs Embedded Size (Quadrant b) ### Overview A line chart comparing loss metrics across different embedded sizes (38M to 1236M) for two model configurations. ### Components/Axes - **X-axis**: Embedded Size (38M, 77M, 154M, 309M, 618M, 1236M) - **Y-axis**: Loss (3.05 to 3.30) - **Data Series**: - Red dashed line: MLA-Base - Blue line: EG-MLA-Base ### Detailed Analysis 1. **MLA-Base** (Red dashed): Maintains constant loss of ~3.30 across all embedded sizes 2. **EG-MLA-Base** (Blue): - Starts at ~3.32 at 38M - Decreases to ~3.22 at 77M - Drops to ~3.12 at 154M - Sharp decline to ~3.05 at 309M - Slight increase to ~3.07 at 618M - Final drop to ~3.05 at 1236M ### Key Observations - EG-MLA-Base shows significant loss reduction with larger embeddings - Loss reduction plateaus at ~3.05 beyond 309M embedded size - MLA-Base maintains stable but higher loss (~3.30) ### Interpretation The EG-MLA-Base configuration demonstrates superior scalability, achieving 17% loss reduction compared to MLA-Base at maximum embedded size. The performance plateau at 309M suggests diminishing returns beyond this point, indicating optimal resource allocation at this scale. --- ## Bar Chart: KV Cache Efficiency (Quadrant c) ### Overview A comparative bar chart showing KV cache efficiency (K/Token #Elem) for three attention mechanisms. ### Components/Axes - **X-axis**: Attention Mechanisms (MHA, MLA-Base, EG-MLA-Base) - **Y-axis**: KV Cache Elements (0-15) - **Annotations**: - EG-MLA-Base shows 59.9% reduction vs MLA-Base - EG-MLA-Base shows 91.6% reduction vs MHA ### Detailed Analysis 1. **MHA**: Highest KV cache usage at ~15 elements 2. **MLA-Base**: ~5 elements (66.7% reduction vs MHA) 3. **EG-MLA-Base**: ~0.75 elements (95% reduction vs MHA) ### Key Observations - EG-MLA-Base achieves dramatic KV cache efficiency improvements - MHA requires 20x more elements than EG-MLA-Base - MLA-Base shows intermediate efficiency gains ### Interpretation The EG-MLA-Base configuration reduces KV cache requirements by nearly 92% compared to traditional MHA, demonstrating significant memory efficiency gains. This suggests potential for 10-15x memory savings in large-scale deployments. --- ## Bar Chart: Decoding Latency (Quadrant d) ### Overview A comparative bar chart showing decoding latency (ms/Token) for two model configurations. ### Components/Axes - **X-axis**: Model Configurations (MLA-Base, EG-MLA-Base) - **Y-axis**: Decoding Latency (0-25 ms/Token) - **Data Series**: - MLA-Base: ~25 ms/Token - EG-MLA-Base: ~26 ms/Token ### Detailed Analysis 1. **MLA-Base**: Consistent latency of ~25 ms/Token 2. **EG-MLA-Base**: Slightly higher latency at ~26 ms/Token ### Key Observations - Both configurations show nearly identical decoding performance - EG-MLA-Base incurs minimal latency penalty despite efficiency gains ### Interpretation The near-parity in decoding latency between configurations suggests that the memory efficiency gains from EG-MLA-Base do not come at the cost of computational speed, making it a favorable choice for resource-constrained environments. --- ## Cross-Quadrant Analysis ### Systemic Trends 1. **Performance-Resource Tradeoff**: Larger embeddings improve accuracy but increase memory requirements 2. **Efficiency Gains**: EG-MLA-Base achieves 91.6% KV cache reduction with only 4% latency increase 3. **Scalability Patterns**: All models show diminishing returns beyond 309M embedded size ### Technical Implications - The EG-MLA-Base configuration offers optimal balance between performance and efficiency - MLA-256 provides best accuracy-to-resource ratio for moderate-scale applications - MHA remains inefficient for modern deployment requirements ### Anomalies - The slight loss increase at 618M embedded size in EG-MLA-Base (3.07 vs 3.05 at 309M) suggests potential optimization opportunities in intermediate-scale configurations.