Understanding LLM Inference Flow A Visual Guide

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“Inference is where Generative AI meets the real world.”
— Moustafa Mahmoud

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Overview

In this article, we explore a simplified LLM Inference Flow using diagrams and code snippets. This helps developers and architects visualize the steps involved when a user sends a query to a deployed Large Language Model.

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High-Level Inference Flow

flowchart TD
    A[User Request] --> B[API Gateway]
    B --> C[Pre-processing]
    C --> D[LLM Inference Engine]
    D --> E[Post-processing]
    E --> F[Response to User]

• User Request: Input query or prompt.
• API Gateway: Entry point for handling requests, rate-limiting, authentication.
• Pre-processing: Input cleaning, prompt engineering.
• LLM Inference Engine: Actual model performing generation.
• Post-processing: Filtering, formatting, safety checks.
• Response: Final output sent back to the user.

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Hybrid Deployment Example

graph LR
    subgraph Cloud
        A1[Model Hosting (Azure OpenAI)]
        A2[API Gateway]
    end
    subgraph On-Prem
        B1[Sensitive Data Pre-processing]
    end
    subgraph Edge
        C1[Lightweight Post-processing]
    end
    
    A2 --> A1
    B1 --> A2
    A1 --> C1
    C1 --> User[Final User Response]

⚠ Hybrid architectures enable flexible deployment while maintaining compliance and performance.

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Code Example

import openai

def query_llm(prompt):
    response = openai.ChatCompletion.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": prompt}]
    )
    return response['choices'][0]['message']['content']

# Example Usage
result = query_llm("Explain LLM inference flow in simple terms.")
print(result)

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Key Takeaways

• 📊 Inference Pipeline involves multiple stages.
• ☁ Hybrid deployment offers flexibility.
• 🔐 Pre/Post-processing ensures safety, privacy, and compliance.
• ⚙ Mermaid diagrams provide great visualizations for architecture flows.

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Stay tuned for more visual AI architecture breakdowns!

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