Navigating LLM deployment: Tips, tricks, and techniques
summary
Regulated customers block public AI APIs, so teams move to self-hosting to keep data and IP on-prem or in VPCs and to avoid rate limits. The stack includes open‑source models from HuggingFace, production inference engines (not Ollama), quantization, speculative decoding and monitoring. Benchmarks claim a 13B model achieving tokens/sec comparable to a 1B model, and a pharma case reports fine‑tuning doubling accuracy on a chart‑reading task. Operational gaps are infra, governance and model selection.
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chapters (9)
- 0:00LLM deployment challenges
- 3:34Self-hosting AI models
- 6:44Pros and cons of self-hosting
- 11:36Self-hosting vs APIs
- 12:59Self-hosting infrastructure
- 14:25Model selection tips
- 21:27Optimizing inference engines
- 25:01LLM deployment pitfalls
- 26:26Key takeaways
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