## Bar Chart: Performance Metrics vs. Batch Size
### Overview
The chart illustrates the relationship between average batch size (in requests) and three performance metrics: Prefill Time, Decode Time, and Global Slowdown. It shows how these metrics scale with increasing batch sizes from 1 to 256 requests.
### Components/Axes
- **X-axis**: Average Batch Size (reqs) with values: 1, 2, 4, 8, 16, 32, 64, 128, 256.
- **Y-axis (left)**: Time (s) with values from 0 to 12 seconds.
- **Y-axis (right)**: Global Slowdown with values from 1 to 6.
- **Legend**:
- Blue: Prefill Time (bars)
- Orange: Decode Time (bars)
- Red: Global Slowdown (line)
- **Placement**: Legend is top-left; bars are clustered, with the red line overlaid.
### Detailed Analysis
1. **Prefill Time**:
- Consistently flat across all batch sizes.
- Approximate value: ~0.5 seconds (uncertainty ±0.1s).
2. **Decode Time**:
- Starts at ~2 seconds for batch size 1.
- Increases exponentially: ~2s (1), ~2.5s (2), ~3s (4), ~3.5s (8), ~4s (16), ~5s (32), ~7s (64), ~9s (128), ~12s (256).
- Notable jump between 128 and 256 (Δ~3s).
3. **Global Slowdown**:
- Starts at 1x for batch size 1.
- Gradually rises: ~1.2x (2), ~1.3x (4), ~1.5x (8), ~1.7x (16), ~2x (32), ~2.5x (64), ~3.5x (128), ~6x (256).
- Steep acceleration after 128 requests.
### Key Observations
- Prefill Time remains constant regardless of batch size.
- Decode Time grows exponentially, with a critical inflection point at 128 requests.
- Global Slowdown mirrors Decode Time's growth pattern but plateaus earlier (6x at 256).
### Interpretation
The data suggests that **Decode Time is the primary driver of system slowdown**, with diminishing returns at larger batch sizes. The exponential growth in Decode Time after 128 requests indicates potential bottlenecks in processing capacity or algorithmic inefficiencies. Prefill Time's stability implies it is decoupled from batch size, possibly due to parallelization or precomputation. The Global Slowdown metric quantifies the system-wide impact, showing that beyond 128 requests, the slowdown becomes nonlinear, likely due to resource contention or memory limitations. This highlights the need for batch size optimization to balance throughput and latency in high-scale systems.
