AI BriefingAI Inference Engineering Guide
Key point
It covers the concepts and importance of inference engineering, which efficiently deploys and optimizes AI models for real-world services.
Details
When an LLM generates a response, two stages run sequentially on the GPU. The first is the stage that processes the input prompt to generate the first token, and the second is the stage that generates every subsequent token one by one.
These two stages create different bottlenecks inside the hardware. One is limited by Raw Compute, and the other is limited by Memory Bandwidth. Inference Engineering is the technique that addresses this difference to optimize Latency, Throughput, cost, and quality.
In the past, this work was mainly the domain of labs that build AI models directly, but recently, as more companies operate models themselves, it has established itself as a specialized field. In particular, advances in Open Model are driving companies to host models directly instead of using APIs.
Hosting a model directly offers the following benefits.
- Latency: Can be optimized to fit a specific product's workload patterns
- Uptime: High availability can be secured through dedicated deployment
- Cost: When economies of scale are achieved, cost savings of about 80% compared to APIs are possible
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