Dear CIO,

While Generative AI is rapidly reshaping the enterprise landscape, confusion remains among many executives regarding the distinction between training and inference in large language models. While both processes typically require GPU resources, their infrastructure demands, costs, and operational profiles differ dramatically. For CIOs, understanding the core differences is essential to optimize resource allocation, improve performance, and control costs in AI deployments. Let's break down the key contrasts between training and inference and explore how infrastructure considerations play a crucial role in successful enterprise AI strategies.

Best Regards,
John, Your Enterprise AI Advisor

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Understanding Inference vs. Training for Generative AI

Why Understanding the AI Lifecycle is Crucial for Enterprise Leaders

As we have discussed, Generative AI is reshaping enterprises. However, many executives might not understand the difference between training and inference with large language models (LLMs). While both typically require GPUs for models, they have vastly different workloads, cost implications, and infrastructure requirements. 

In most enterprise use cases, organizations do not train new foundational or large models; instead, they run inferences on pre-trained ones. With average GPU utilization rates hovering around 15% in many enterprises, understanding the differences in operations and infrastructure associated with these two tasks is critical to optimizing costs, performance, and resource allocation.

Inference vs. Training: The Core Differences

Training: High-Intensity, Iterative Computation

Training large language models (LLMs) similar to GPT-4o or Llama 3 is an iterative optimization process that relies on gradient descent and backpropagation. 

Gradient descent is an optimization algorithm that minimizes the error in a machine learning model by iteratively adjusting its parameters. Backpropagation is a technique that computes gradients efficiently by propagating errors backward through the layers of a neural network. This helps update weights and improve model performance. 

During training, the model continuously adjusts its parameters by processing massive datasets, refining its ability to generate accurate and meaningful outputs. This process involves extensive computations, requiring high-performance GPUs or specialized AI accelerators to efficiently handle matrix operations and tensor calculations. Due to the enormous computational demand, training a modern LLM takes significant time, depending on the model size and available hardware. The process is also highly memory-intensive, necessitating GPUs with high-bandwidth memory (HBM) to manage the storage and rapid retrieval of intermediate activations, gradients, and model weights. Large-scale distributed computing infrastructure is typically required to coordinate thousands of GPUs, ensuring that training can progress efficiently across multiple nodes while balancing workload distribution.

Enterprises typically do not train their models because of the significant computational demands and necessary expertise. Instead, they fine-tune models, use Retrieval Augmentation Generation (RAG) and Cache Augmented Generation (GAG), or use pre-trained models from providers such as OpenAI, Google, or Anthropic.

Inference: The Enterprise Standard

Inference runs a trained model to generate responses based on new input data. Unlike training, which involves iterative weight updates, inference requires only a single or a limited set of forward passes through the model, making it computationally less demanding. However, for enterprise applications serving millions of users, such as chatbots, virtual assistants, or document summarization tools, inference may still require substantial GPU resources to maintain performance at scale.

A key requirement for inference is low-latency response times, which ensure real-time or near-real-time interactions in applications like customer service automation or AI-driven search tools. To achieve efficiency, inference workloads often incorporate specialized optimizations, such as a Mixture of Experts (MOE) utilizing quantization (reducing numerical precision for faster computations), optimized GPU kernels, and model distillation (using smaller, fine-tuned versions of large models to reduce computational overhead). These techniques help enterprises balance speed, accuracy, and cost-effectiveness when deploying generative AI solutions.

For most enterprises, investments should be focused on inference, optimizing for scalability, speed, and efficiency.

Inference Usage in Enterprise Applications

The prevailing use of inference over training is evident across various industries where real-time decision-making, cost efficiency, and scalability are critical. Unlike training, which occurs periodically and requires significant computational resources, inference operates continuously, enabling enterprises to derive insights and automate processes with minimal latency.

The paper Rethinking Concerns About AI’s Energy (Jan 2024) primarily argues against exaggerated claims about artificial intelligence (AI) 's energy consumption. It addresses concerns that AI will cause an unsustainable surge in electricity demand. A central theme is the distinction between the energy required for training AI models and the energy required for inference, emphasizing that most AI-related energy consumption comes from inference rather than training.

The paper explains that training large AI models can be energy-intensive but is a one-time cost. In contrast, inference—the process of using a trained AI model to generate outputs—occurs continuously and, in most cases, dominates the long-term energy footprint of AI applications. It cites estimates from Amazon Web Services and Schneider Electric, which indicate that inference accounts for 80-90% of the energy costs of AI in data centers. Additionally, studies by Meta suggest that inference represents around 65% of the carbon footprint for large language models (LLMs) and an even higher proportion for other AI applications. The paper contends that popular narratives tend to overemphasize the energy required for training while overlooking inference, leading to misleading policy discussions.

The author argues that AI's energy use concerns should be contextualized within broader trends of increasing computational efficiency. Hardware improvements, optimization techniques, and the transition to more energy-efficient AI models are expected to mitigate energy consumption growth. Moreover, AI is positioned as a tool that can contribute to sustainability efforts, such as optimizing energy grids, reducing emissions in logistics, and enhancing industrial efficiency. The paper recommends policymakers focus on energy transparency standards and avoid regulations that might inadvertently increase AI’s energy footprint while attempting to regulate fairness, safety, or bias in AI models.​

Leveraging MLPerf Storage to Understand Training vs. Inference

The MLPerf Storage benchmark provides essential insights into how these workloads interact with storage systems, helping CIOs optimize AI infrastructure. Understanding storage-specific performance metrics from MLPerf can help explain why training demands high throughput and large storage capacity while inference prioritizes low latency and efficient retrieval.

MLPerf Storage Insights on Training Workloads

As discussed, training workloads involve extensive data preprocessing, high bandwidth data streaming, and frequent checkpoints. MLPerf Training benchmarks reveal that training models like GPT-4o or Llama 3 require massive datasets stored in high-performance distributed file systems or high-speed NVMe SSDs to avoid bottlenecks. These benchmarks emphasize:

• High Throughput Needs: Training models require continuous, high-speed access to data, often exceeding 10 GB/s in large-scale distributed training environments.

• Checkpointing Overheads: Regular checkpointing ensures fault tolerance but also contributes to massive storage demands, with some models requiring over 10 GB per checkpoint.

• Parallel Storage Optimization: Distributed storage solutions or parallel file systems to reduce storage I/O bottlenecks during model training.

By analyzing MLPerf Storage results, CIOs can better understand why enterprises generally do not train large models from scratch. The storage and compute costs associated with high-performance training infrastructure make adopting pre-trained models more practical.

MLPerf Storage Insights on Inference Workloads

Inference workloads, while less storage-intensive than training, still require optimized storage for rapid access to pre-trained models and real-time query processing. MLPerf Inference benchmarks provide insights into:

• Latency-Sensitive Data Access: Unlike training, where throughput dominates, inference minimizes latency. Low-latency storage, such as NVMe SSDs or memory-optimized caching, is critical for real-time applications.

• Model Compression Impact: MLPerf Storage benchmarks highlight the benefits of quantization and model pruning, which reduce storage footprint and improve retrieval times for inference workloads.

• Edge Inference Challenges: Storage constraints on edge devices necessitate model compression and efficient data loading strategies. MLPerf results demonstrate how optimizing storage configurations can significantly impact response times for AI-driven applications.

MLPerf Storage data emphasizes the need for optimized storage architectures that balance speed, efficiency, and cost for businesses focusing on inference. By leveraging MLPerf insights, CIOs can make informed choices regarding cloud-based storage, edge computing deployments, and hardware selection to enhance AI workloads. MLPerf Storage benchmarks provide empirical evidence highlighting the essential differences between training and inference. Training requires high-throughput, large-scale storage, while inference needs low-latency, optimized storage solutions. Companies can use these insights to improve infrastructure planning, lower AI deployment costs, and guarantee optimal performance for real-world generative AI applications.

The CIO Takeaway

CIOs may not need to allocate funds for massive GPU clusters to train LLMs. Instead, optimize inference pipelines, reduce latency, and scale efficiently. The enterprises succeeding in Generative AI are typically not building models from scratch—they’re deploying and fine-tuning pre-trained models for real-world applications. Understanding these differences can cut costs, improve performance, and future-proof your AI strategy.

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