# Technical Data Extraction: Llama 7B Performance Metrics
## 1. Document Metadata
* **Title:** Llama 7B, Batch Size: 1, Sequence Length: 1024
* **Primary Language:** English
* **Image Type:** Combined Line Graph and Stacked Bar Chart
## 2. Component Isolation
### A. Header
* **Text:** "Llama 7B, Batch Size: 1, Sequence Length: 1024"
* **Context:** Defines the model architecture and specific inference parameters used for the data collection.
### B. Main Chart Area (Axes)
* **Y-Axis Label:** "Normalized Latency/ Acc. Rate/ Speedup"
* **Y-Axis Scale:** Linear, ranging from 0.0 to 3.0+ (increments of 0.5).
* **X-Axis Label:** "Number of Candidate Tokens"
* **X-Axis Markers (Categories):** 1, 16, 32, 48, 64, 80, 96, 112.
### C. Legend
* **Blue Star ($\star$):** Simulated Acc. Rate (Line)
* **Green Star ($\star$):** Simulated Speedup (Line)
* **Dark Purple Block:** qk/pv ar (Stacked Bar component)
* **Medium Purple Block:** qkv linear ar (Stacked Bar component)
* **Light Pink/Lavender Block:** up/gate/down ar (Stacked Bar component)
---
## 3. Data Series Analysis & Trend Verification
### Series 1: Simulated Acc. Rate (Blue Star / Dashed Blue Line)
* **Visual Trend:** Consistent upward slope. The rate of increase is steepest between 1 and 32 tokens, then continues to climb at a shallower, steady gradient through to 112 tokens.
* **Estimated Data Points:**
* 1: ~1.0
* 16: ~2.4
* 32: ~2.7
* 48: ~2.9
* 64: ~3.05
* 80: ~3.15
* 96: ~3.25
* 112: ~3.3
### Series 2: Simulated Speedup (Green Star / Dashed Green Line)
* **Visual Trend:** Initial rapid growth matching the Acc. Rate until 32 tokens. It plateaus between 64 and 112 tokens, showing a slight dip at 80 before stabilizing.
* **Estimated Data Points:**
* 1: 1.0 (Baseline)
* 16: ~2.4
* 32: ~2.7
* 48: ~2.85
* 64: ~2.95
* 80: ~2.8
* 96: ~2.85
* 112: ~2.85
### Series 3: Normalized Latency Components (Stacked Bars)
* **Visual Trend:** The total height of the bars (representing total normalized latency) remains very close to 1.0 for candidate tokens 1 through 64. Starting at 80 tokens, the total latency begins to increase visibly, reaching approximately 1.2 by 112 tokens.
* **Component Breakdown:**
* **qk/pv ar (Dark Purple):** Smallest contributor; remains relatively constant with a very slight increase as token count grows.
* **qkv linear ar (Medium Purple):** Middle contributor; remains stable until 80 tokens, where it expands slightly.
* **up/gate/down ar (Light Pink):** Largest contributor; remains stable until 80 tokens, then shows the most significant growth in height, driving the overall latency increase.
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## 4. Data Table Reconstruction (Estimated Values)
| Number of Candidate Tokens | Simulated Acc. Rate (Blue) | Simulated Speedup (Green) | Total Normalized Latency (Bar Height) |
| :--- | :--- | :--- | :--- |
| **1** | 1.0 | 1.0 | 1.0 |
| **16** | 2.4 | 2.4 | 1.0 |
| **32** | 2.7 | 2.7 | 1.0 |
| **48** | 2.9 | 2.85 | 1.02 |
| **64** | 3.05 | 2.95 | 1.04 |
| **80** | 3.15 | 2.8 | 1.13 |
| **96** | 3.25 | 2.85 | 1.15 |
| **112** | 3.3 | 2.85 | 1.18 |
---
## 5. Technical Summary
The chart illustrates the performance of a Llama 7B model using speculative decoding or a similar candidate-token-based acceleration method.
* **Efficiency Peak:** The "Simulated Speedup" tracks closely with the "Simulated Acceptance Rate" until approximately 32-48 candidate tokens.
* **Diminishing Returns:** Beyond 64 tokens, the "Simulated Speedup" plateaus and even slightly regresses. This is explained by the "Normalized Latency" bars, which show that the computational overhead (specifically in the `up/gate/down` and `qkv linear` layers) begins to increase significantly after 64 tokens, offsetting the gains from a higher acceptance rate.
