turbovec Review: 4x Memory Compression for RAG (TurboQuant 2026)
You’re building a RAG pipeline with a million documents. Each vector is 1536 floats — OpenAI ada-002 style. And that’s about 6 KB per vector in float32. Do the math: 10 million vectors = 31 GB of RAM just for the index, before your application code even starts. That’s the wall a lot of self-hosted RAG projects hit. But Pinecone costs a fortune. FAISS needs a training phase and still takes ~8 GB. I’ve been tracking tools that tackle these memory bottlenecks — my Headroom review covers LLM context compression from a different angle. So when I saw turbovec hit #2 on GitHub Trending with 10.2k★ in its first week, I had to try it. ...