he rigs up 10 RTX 4090s, runs protein folding simulations, loads local LLMs for privacy, talks about quantization, multi-GPU sharding, RAG, how SLMs are underrated, AND says he’s fine-tuning his own model. bro knows AI more than most of the AI influencers here. https://t.co/pbsKDhR06C

A solution is to self-host an open-sourced or custom fine-tuned LLM. Opting for a self-hosted model can reduce costs dramatically - but with additional development time, maintenance overhead, and possible performance implications. Considering self-hosted solutions requires weighing these different trade-offs carefully.

However development time, and maintenance can offset these savings. Hiring skilled data scientists, machine learning engineers, and DevOps professionals can be expensive and time consuming. Using available resources for “reimplementing” solutions hinder innovation and lead to a lack of focus. Since You not longer work on improving your model or... See more

Top considerations when choosing foundation models

Accuracy

Cost

Latency

Privacy

Top challenges when deploying production AI

Serving cost

Evaluation

Infra reliability

Model quality

The core idea is simple: put the thinking (compute) as close as possible to the data and the person using it. Apple’s hardware, the MLX toolkit, and real‐world examples like Stable Diffusion running on a Mac, show that this isn’t science fiction, it’s here. Try running Apple’s Foundation Model on your iPhone in airplane mode and prepare to be... See more