## Line Chart: Throughput and Memory Usage Comparison of X-EcoMLA-8B and Llama3.1-8B Models
### Overview
The chart compares the performance and resource efficiency of two language models (X-EcoMLA-8B and Llama3.1-8B) across varying batch sizes (8 to 1024). Two metrics are plotted:
1. **Throughput** (tokens/second) on the left y-axis (blue lines).
2. **Peak Memory Usage** (GB) on the right y-axis (red lines).
The chart uses solid lines for throughput and dashed lines for memory usage, with distinct colors for each model.
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### Components/Axes
- **X-Axis**: Batch Size (logarithmic scale: 8, 32, 64, 128, 256, 512, 1024).
- **Left Y-Axis**: Throughput (tokens/second) ranging from 0 to 5,500.
- **Right Y-Axis**: Peak Memory Usage (GB) ranging from 0 to 140.
- **Legend**: Located in the bottom-right corner, with:
- **Blue solid line**: X-EcoMLA-8B Throughput.
- **Cyan dashed line**: Llama3.1-8B Throughput.
- **Red solid line**: X-EcoMLA-8B Memory.
- **Orange dashed line**: Llama3.1-8B Memory.
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### Detailed Analysis
#### Throughput Trends
1. **X-EcoMLA-8B** (blue solid line):
- At batch size 8: ~500 tokens/sec.
- At batch size 32: ~1,200 tokens/sec.
- At batch size 64: ~3,200 tokens/sec.
- At batch size 128: ~3,900 tokens/sec.
- At batch size 256: ~4,600 tokens/sec.
- At batch size 512: ~5,100 tokens/sec.
- At batch size 1024: ~5,300 tokens/sec.
- **Trend**: Linear increase with batch size, plateauing near 5,300 tokens/sec.
2. **Llama3.1-8B** (cyan dashed line):
- At batch size 8: ~300 tokens/sec.
- At batch size 32: ~1,500 tokens/sec.
- At batch size 64: ~2,000 tokens/sec.
- At batch size 128: ~2,100 tokens/sec.
- At batch size 256: ~2,100 tokens/sec.
- At batch size 512: ~2,100 tokens/sec.
- At batch size 1024: ~2,100 tokens/sec.
- **Trend**: Rapid initial growth, plateauing at ~2,100 tokens/sec after batch size 64.
#### Memory Usage Trends
1. **X-EcoMLA-8B** (red solid line):
- At batch size 8: ~20 GB.
- At batch size 32: ~30 GB.
- At batch size 64: ~40 GB.
- At batch size 128: ~60 GB.
- At batch size 256: ~80 GB.
- At batch size 512: ~100 GB.
- At batch size 1024: ~120 GB.
- **Trend**: Linear increase with batch size.
2. **Llama3.1-8B** (orange dashed line):
- At batch size 8: ~10 GB.
- At batch size 32: ~15 GB.
- At batch size 64: ~20 GB.
- At batch size 128: ~25 GB.
- At batch size 256: ~30 GB.
- At batch size 512: ~35 GB.
- At batch size 1024: ~40 GB.
- **Trend**: Linear increase with batch size, but significantly lower than X-EcoMLA-8B.
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### Key Observations
1. **Performance Gap**:
- X-EcoMLA-8B consistently outperforms Llama3.1-8B in throughput (e.g., ~5,300 vs. ~2,100 tokens/sec at batch size 1024).
2. **Memory Efficiency**:
- Llama3.1-8B uses ~40% less memory than X-EcoMLA-8B at all batch sizes (e.g., ~40 GB vs. ~120 GB at batch size 1024).
3. **Scaling Behavior**:
- Both models show linear scaling for memory usage, but X-EcoMLA-8B’s throughput scales more effectively.
4. **Plateau Effect**:
- Llama3.1-8B’s throughput plateaus at ~2,100 tokens/sec despite increasing batch sizes beyond 64.
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### Interpretation
- **Trade-off Analysis**:
X-EcoMLA-8B is optimized for high-throughput tasks but requires significantly more memory, making it suitable for resource-rich environments. Llama3.1-8B offers a memory-efficient alternative with moderate throughput, ideal for constrained systems.
- **Scalability Insights**:
The linear memory scaling for both models suggests predictable resource planning, but Llama3.1-8B’s throughput plateau indicates diminishing returns at larger batch sizes.
- **Practical Implications**:
For applications prioritizing speed (e.g., real-time inference), X-EcoMLA-8B is preferable. For memory-constrained scenarios (e.g., edge devices), Llama3.1-8B is more viable.
No textual content or additional languages are present in the image. All data points are approximate, derived from visual alignment with axis markers.
