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Design Guide—Generative AI in the Enterprise - Inferencing
Introduction AI Model Inferencing Architecture Concepts and Components Solution Architecture Validation Results Performance Characterization and Sizing
Overview
Performance test results
Sizing guidelines
Inferencing Operations Dell Professional Services for Generative AI Conclusion References

Overview

Overview

  • Sizing the infrastructure for LLM inference is essential due to the computational demands, high memory requirements, and unique characteristics of these models. Properly sizing the infrastructure ensures efficient handling of LLMs' massive parameter count during inference, avoiding out-of-memory errors and achieving low latency. It enables resource efficiency, scalability, and cost optimization by accommodating varying workloads and selecting optimal configurations. Overall, proper infrastructure sizing for LLM inference ensures optimal performance, scalability, and user experience while managing operational costs effectively and removing any resource bottlenecks.

    To characterize various GPT models and gain insights into their inference performance, our goal is to provide sizing guidelines. Consider several critical factors when sizing an infrastructure for LLM model inference:

    1. Model size in parameters—The number of parameters in the LLM directly impacts its memory footprint and computational requirements. Larger models with more parameters might require more powerful hardware for efficient inference.
    1. Latency—Low latency is crucial for real-time applications and user interactions. Properly sizing the infrastructure helps meet latency targets and ensures fast response times.
    2. Number of concurrent requests—Understanding the expected number of concurrent inference requests is essential for determining the required computational resources and ensuring smooth and responsive inference under varying workloads.
    3. GPU compute and memory use—For GPU-accelerated inference, considering both compute and memory use is critical to optimizing resource allocation and ensuring efficient GPU use.