The efficient frontier of LLM inference
This article explores the concept of the 'efficient frontier' in LLM inference, focusing on the trade-offs between latency, throughput, and model intelligence. It categorizes engineering techniques into those that navigate existing trade-offs and those that push the frontier outward to improve overall system efficiency.
Why it matters
Understanding these trade-offs is critical for engineers and businesses looking to optimize the cost and performance of AI-driven applications in production environments.
In the AI industry, we borrowed the term “efficient frontier” from economists. We use it to talk about managing tradeoffs, most often the tradeoff between cost and capabilities for models. A model is a “frontier model” if it offers the highest degree of intelligence at a given cost or size.
We also have efficient frontiers in inference engineering. Most often, this is expressed as a tradeoff between latency and throughput (which determines cost), though we can also exchange quality for throughput (via quantization, distillation, and pruning) or intelligence for speed (in the form of reasoning level).
There are two types of techniques available to inference engineers:
Techniques which make a tradeoff between two factors to move a deployment along an efficient frontier.
Techniques which push out the entire frontier for a given deployment, creating more overall efficiency which can be allocated to whatever outcome is most beneficial.
Get smarter about the news
Sign up free for a feed built around what you actually care about, Dive Deeper research on any story, and the full text of every article.
Create free accountAlready have an account? Sign in