# Technical Analysis of Large Language Model (LLM) Performance Characteristics This document provides a detailed extraction of data and technical insights from the provided image, which consists of three sub-figures (a, b, and c) analyzing LLM inference stages. --- ## Figure (a): Generation stage is slower ### Component Isolation: Pie Chart * **Type:** Pie Chart comparing time duration of different inference stages. * **Legend (Center Overlay):** * **Dark Grey Circle:** Context (200 tokens) * **Maroon Circle:** Generation (20 tokens) * **Data Points:** * **Context Stage:** Represented by a small dark grey slice at the top. Value: **10 ms**. * **Generation Stage:** Represented by the large maroon section comprising the majority of the circle. Value: **310 ms**. * **Trend/Insight:** Despite having 10x fewer tokens (20 vs 200), the Generation stage takes 31x longer to process than the Context stage, indicating a significant bottleneck in the autoregressive generation phase. --- ## Figure (b): Generation stage is bounded by memory bandwidth ### Component Isolation: Roofline Model Chart * **X-Axis:** Arithmetic Intensity (FLOPs/Byte). Markers: 0, 75, 150, 225, 300. * **Y-Axis:** Peak TFLOPS. Markers: 0, 36, 72, 108, 144, 180. * **Legend/Annotations:** * **Maroon Line:** Represents the performance ceiling. It slopes upward linearly from (0,0) until it hits a plateau at approximately Y=165. * **Dark Grey Dot (at origin):** Labeled "Generation Stage: Arith. Inten. = 1, 1TFLOPS (W16A16)". * **Maroon Dot (on the slope):** Labeled "Generation Stage: Arith. Inten. = 4, 4TFLOPS (W4A16)". * **Context Stage Annotation:** Points to the plateau region. Text: "Context stage: Arith. Inten. >= 165". ### Data Extraction & Trends 1. **Memory Bound Region (Sloped Line):** The Generation stage (both W16A16 and W4A16) falls on the sloped part of the roofline. This indicates performance is limited by memory bandwidth, not compute power. 2. **Compute Bound Region (Plateau):** The Context stage reaches the plateau (approx. 165 TFLOPS), indicating it is compute-bound. 3. **Quantization Effect:** Moving from W16A16 (Weight 16-bit, Activation 16-bit) to W4A16 (Weight 4-bit) increases Arithmetic Intensity from 1 to 4 and increases performance from 1 TFLOPS to 4 TFLOPS, following the memory-bandwidth slope. --- ## Figure (c): Weight loading is more expensive ### Component Isolation: Bar Chart * **Type:** Grouped Bar Chart with a logarithmic Y-axis. * **X-Axis Categories:** Attention, FFN (Feed-Forward Network). * **Y-Axis:** Memory footprint (MB). Scale: $10^{-2}, 10^{-1}, 1, 10, 10^2, 10^3$. * **Legend (Top):** * **Maroon Square:** Weight * **Dark Grey Square:** Activation ### Data Table Reconstruction | Category | Component | Value (MB) | Ratio (Weight/Activation) | | :--- | :--- | :--- | :--- | | **Attention** | Weight (Maroon) | 134 | 79x | | **Attention** | Activation (Grey) | 1.7 | - | | **FFN** | Weight (Maroon) | 271 | 1700x | | **FFN** | Activation (Grey) | 0.2 | - | ### Trend Verification * **Weight Dominance:** In both Attention and FFN modules, the memory footprint of Weights (Maroon) significantly dwarfs the Activations (Grey). * **FFN Disparity:** The disparity is most extreme in the FFN layer, where weights require 1700x more memory than activations. * **Visual Trend:** The maroon bars are consistently much taller than the grey bars across the logarithmic scale, emphasizing that weight loading is the primary memory bottleneck during the generation stage.