## Line Chart: Context Memory Usage Comparison Between Existing Systems and ForkKV
### Overview
The chart compares memory usage (in GB) for two systems—Existing Systems and ForkKV—as the number of concurrent agents increases. It highlights ForkKV's ability to handle 32x more agents within the same GPU capacity while maintaining lower memory usage.
### Components/Axes
- **X-axis**: Agents (#NUM) with markers at 0, 16, 32, 48, 64.
- **Y-axis**: Context Memory Usage (GB) with markers at 0, 8, 16.
- **Legend**:
- Blue line: Existing Systems
- Teal line: ForkKV
- **Annotations**:
- "Run 32x concurrent agents within same GPU capacity" (pointing to ForkKV's line at 32 agents).
- "Base cache is computed once and shared across agents" (pointing to ForkKV's line at 0 agents).
### Detailed Analysis
- **Existing Systems (Blue Line)**:
- Starts at **8 GB** for 0 agents.
- Rises sharply to **16 GB** at 16 agents.
- Remains flat at 16 GB for all agent counts beyond 16.
- **ForkKV (Teal Line)**:
- Starts at **0 GB** for 0 agents.
- Increases linearly to **8 GB** at 64 agents.
- Intermediate points: 4 GB at 16 agents, 6 GB at 32 agents, 7 GB at 48 agents.
### Key Observations
1. **Memory Efficiency**: ForkKV uses significantly less memory than Existing Systems across all agent counts.
2. **Scalability**: ForkKV scales linearly with agents (0–8 GB for 0–64 agents), while Existing Systems plateau at 16 GB after 16 agents.
3. **Base Cache Optimization**: The annotation suggests ForkKV avoids redundant cache computations, contributing to lower memory usage.
### Interpretation
The data demonstrates ForkKV's superior memory efficiency and scalability compared to Existing Systems. While Existing Systems experience a steep memory increase (8 GB → 16 GB) when adding 16 agents, ForkKV maintains sub-8 GB usage even at 64 agents. This implies ForkKV is better suited for high-concurrency workloads, as it avoids memory saturation and leverages shared caching. The linear scaling of ForkKV suggests predictable resource allocation, whereas Existing Systems may require GPU upgrades or optimizations to handle increased agents without excessive memory overhead.
