4 min read
3072-dim is the new hard mode for vector search
text-embedding-3-large produces 3,072-dim vectors, and most vector pipelines truncate them to stay performant. Here's what happened when we benchmarked the full dimension end-to-end.
Tag
#benchmarks
3 posts tagged benchmarks.
- 5 min read
How GitDB cut our AI coding bill by 95%
A walk-through of the actual token math — where the savings come from, what they don't help with, and how I'd reproduce them in your codebase.
- 6 min read
100% recall, on disk, no GPU
The conventional wisdom on vector search is that you have to pick two of three — recall, speed, footprint. Here's what our engine does on the dbpedia-openai benchmark.