510: Inference Optimization¶

Chapter Overview

A model's real-world usefulness is ultimately determined by two factors: how quickly it responds (latency) and how much it costs to run (cost). Inference Optimization is the practice of applying techniques at the model, hardware, and service levels to improve these characteristics.

In a production environment, the component that runs the model is the inference server, which is part of a broader inference service.

The Two Bottlenecks of AI Workloads¶

To optimize performance, we first need to understand what is slowing things down. AI workloads are typically limited by one of two bottlenecks:

graph LR
    subgraph "Compute-Bound"
        A[Raw Processing Power<br/>FLOPS Limited] --> B[Image Generation<br/>Training<br/>Complex Simulations]
    end

    subgraph "Memory-Bandwidth-Bound"
        C[Data Movement Speed<br/>Memory → Processor] --> D[Autoregressive Text Generation<br/>LLM Inference<br/>Sequential Processing]
    end

    style A fill:#ffcdd2,stroke:#d32f2f
    style C fill:#c8e6c9,stroke:#388e3c
    style D fill:#fff3e0,stroke:#f57c00

1. Compute-Bound¶

The limiting factor is the raw computational power (FLOPS) of the hardware. The processor cannot perform the required calculations fast enough.

Typical Tasks: - Image generation - Training large models - Complex scientific simulations

2. Memory-Bandwidth-Bound¶

The limiting factor is the speed at which data can be moved between the GPU's memory and its processing cores. The processor is waiting for data to arrive.

Typical Tasks: - Autoregressive text generation (the core task of LLMs) is almost always memory-bandwidth-bound

Key Insight

Since LLM inference is memory-bandwidth bound, simply using a GPU with more theoretical FLOPS (computing power) might not make it faster. A GPU with higher memory bandwidth is often a better choice.

The Two Types of Inference¶

Inference services are typically designed for one of two use cases:

graph TD
    A[Inference Service] --> B[Online Inference]
    A --> C[Batch Inference]

    B --> D[Optimized for<br/>Low Latency]
    B --> E[Real-time Processing<br/>User Waiting]

    C --> F[Optimized for<br/>High Throughput]
    C --> G[Batch Processing<br/>Cost Efficient]

    D --> H[Example: Chatbot<br/>Interactive AI]
    F --> I[Example: Document<br/>Summarization]

    style B fill:#e3f2fd,stroke:#1976d2
    style C fill:#e8f5e8,stroke:#388e3c
    style H fill:#fff3e0,stroke:#f57c00
    style I fill:#fce4ec,stroke:#c2185b

Online Inference¶

Optimization Goal: Low latency
Characteristics: Requests processed immediately as users are actively waiting
Example: Real-time chatbot conversations

Batch Inference¶

Optimization Goal: High throughput and low cost
Characteristics: Multiple requests grouped and processed together to maximize hardware utilization
Trade-off: Higher latency per request, but lower overall cost per request
Example: Periodically generating summaries for large document collections

Core Optimization Strategies¶

Optimizing inference involves a multi-layered approach:

graph TD
    A[Inference Optimization] --> B[Model-Level Optimization]
    A --> C[Service-Level Optimization]
    A --> D[Hardware Selection]

    B --> E[Quantization<br/>Pruning<br/>Knowledge Distillation]
    C --> F[Batching<br/>Prompt Caching<br/>Load Balancing]
    D --> G[GPU Selection<br/>TPU Usage<br/>Specialized Hardware]

    E --> H[Smaller Models<br/>Faster Inference]
    F --> I[Better Utilization<br/>Lower Cost]
    G --> J[Optimal Performance<br/>Cost Balance]

    style B fill:#e3f2fd,stroke:#1976d2
    style C fill:#e8f5e8,stroke:#388e3c
    style D fill:#fff3e0,stroke:#f57c00

Model-Level Optimization¶

Techniques that modify the model itself to make it more efficient: - Quantization: Reduce numerical precision - Pruning: Remove unnecessary parameters - Knowledge Distillation: Train smaller models to mimic larger ones

Service-Level Optimization¶

Techniques that improve how the inference server handles requests: - Batching: Process multiple requests together - Prompt Caching: Store and reuse common computations - Load Balancing: Distribute requests across multiple instances

Hardware Selection¶

Choosing the right accelerator for your specific workload: - GPU Selection: Balance memory bandwidth vs. compute power - TPU Usage: Specialized for certain AI workloads - CPU Optimization: For smaller models and specific use cases

Optimization Decision Framework¶

flowchart TD
    A[Start Optimization] --> B{What's the Primary Goal?}

    B -->|Reduce Latency| C[Focus on Model<br/>Compression]
    B -->|Reduce Cost| D[Focus on Service<br/>Optimization]
    B -->|Scale Users| E[Focus on Hardware<br/>Selection]

    C --> F[Quantization<br/>Pruning]
    D --> G[Batching<br/>Caching]
    E --> H[Load Balancing<br/>GPU Clusters]

    F --> I[Measure Performance]
    G --> I
    H --> I

    I --> J{Goals Met?}
    J -->|No| K[Combine Strategies]
    J -->|Yes| L[Deploy & Monitor]

    K --> I

    style C fill:#ffcdd2,stroke:#d32f2f
    style D fill:#c8e6c9,stroke:#388e3c
    style E fill:#fff3e0,stroke:#f57c00

Best Practices¶

Start with Measurement¶

Establish baseline performance metrics
Identify current bottlenecks
Set clear optimization targets

Optimize Incrementally¶

Apply one optimization technique at a time
Measure impact before adding more changes
Maintain model quality benchmarks

Consider Trade-offs¶

Speed vs. Quality: Faster models may sacrifice accuracy
Cost vs. Performance: Cheaper solutions may increase latency
Complexity vs. Maintainability: Advanced optimizations may be harder to manage

Common Optimization Mistakes¶

Over-Optimization¶

Applying too many techniques simultaneously
Optimizing beyond actual requirements
Sacrificing model quality for marginal gains

Wrong Bottleneck Focus¶

Optimizing compute when memory-bound
Focusing on throughput when latency matters
Ignoring real-world usage patterns

Insufficient Testing¶

Not validating model quality after optimization
Skipping performance testing under load
Ignoring edge cases and failure modes

Next Steps¶

To optimize a system effectively, we first need to know how to measure it properly.

Continue Learning: - 📊 Inference Performance Metrics: Learn how to measure optimization success - 🔧 Model Compression & Quantization: Understand model-level optimization techniques - ⚡ Service-Level Optimization: Explore batching, caching, and scaling strategies

Key Takeaways¶

✅ Understand your bottleneck: Compute-bound vs. memory-bound
✅ Choose the right optimization: Online vs. batch inference
✅ Apply layered approach: Model, service, and hardware optimization
✅ Measure before and after: Always validate optimization impact
✅ Consider trade-offs: Balance speed, cost, and quality