For vector generation I started using Meta-LLama-3-8B in april 2024 with Python and Transformers for each text chunk on an RTX-A6000. Wow that thing was fast but noisy and also burns 500W. So a year ago I switched to an M1 Ultra and only had to replace Transformers with Apple's MLX python library. Approximately the same speed but less heat and noise. The Llama model has 4k dimensions so at fp16 thats 8 kilobyte per chunk, which I store in a BLOB column in SQLite via numpy.save(). Between running on the RTX and M1 there is a very small difference in vector output but not enough for me to change retrieval results, regenerate the vectors or change to another LLM.

For retrieval I load all the vectors from the SQlite database into a numpy.array and hand it to FAISS. Faiss-gpu was impressively fast on the RTX6000 and faiss-cpu is slower on the M1 Ultra but still fast enough for my purposes (I'm firing a few queries per day, not per minute). For 5 million chunks memory usage is around 40 GB which both fit into the A6000 and easily fits into the 128GB of the M1 Ultra. It works, I'm happy.