#Google

turbovec is a Rust-backed vector index with Python bindings that implements Google's TurboQuant algorithm — compressing 31 GB of float32 embeddings down to 4 GB while beating FAISS on search speed. No training, no warmup, no drama. Here's why it matters for anyone building RAG pipelines.

Google's Gemma 4 12B is a fully open-weights, encoder-free multimodal model that handles text, images, and audio — and fits inside 16GB of VRAM. Here's what makes the architecture interesting and whether it changes anything for local AI workflows.