Tutorials on Inference System Bottlenecks

Watch: AI Inference: The Secret to AI's Superpowers by IBM Technology Inference systems have become the critical factor determining the success or failure of AI deployments, especially as large language models (LLMs) grow in size and complexity. Unlike training, which is a one-time computational expense, inference costs accumulate with every user query, often dominating the total cost of ownership for AI systems. For example, OpenAI’s financial disclosures reveal losses exceeding $5 billion due to inference expenses alone, highlighting the economic stakes of optimizing this phase. As AI models scale, the shift from training to inference as the primary bottleneck is reshaping how businesses design, deploy, and manage AI systems. As mentioned in the Limitations of Scaling Models for Performance section, scaling models eventually hits a wall where additional parameters or computational power yield minimal gains. The economics of AI are tilting sharply toward inference. While training a model like GPT-4 might cost millions, inference demands a continuous, granular allocation of resources for every request. This is because inference involves prefill (processing input tokens in parallel) and decode (generating output tokens sequentially), each with distinct computational needs. Prefill is compute-bound, while decode is memory-bandwidth-bound-a duality that complicates optimization. For instance, a GPU with high memory bandwidth can improve decode speed even if its raw compute power is lower. Companies like DeepSeek have demonstrated how architectural choices, such as hybrid parallelism strategies, can mitigate these bottlenecks. Yet, the rising cost of high-bandwidth memory (HBM) compared to standard DDR further strains budgets, as noted in industry projections showing a 35% HBM price increase by 2025. Building on concepts from the Latency vs Throughput: The Core Trade-off section, optimizing one aspect often requires trade-offs in the other.