What AI Engineers Get Wrong When Deploying Open-Source Models to Product
Since you’re reading this, I’m guessing you’re deploying open-source models to production and wondering if you’re doing everything correctly. What should you optimize? What could be overkill?
Giving all you’ve got to a single LLM that does text generation and treating everything else around it as secondary infra may seem like a logical choice at this point. But in an agent, the opposite is often true. This surrounding infrastructure layer is where actually where most of the cost and latency accumulate.
Lucky for you, this article will cover the four most common mistakes teams make when serving open-source models in production and how to fix each one.
TL;DR:Serving open-source models in production rarely means running a single model. It often involves many small models running on shared hardware. Fix the retrieval layer, improve GPU utilization, and do the break-even math, and the choice between frontier and open models will...
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