mindtro/semafold
Vector compression with TurboQuant codecs for embeddings, retrieval, and KV-cache. 10x compression, pure NumPy core — optional GPU acceleration via PyTorch (CUDA/MPS) or MLX (Metal).
This project helps AI infrastructure teams reduce the storage footprint of numerical AI data. It takes large embedding vectors or key-value tensors and outputs significantly smaller, compressed versions, maintaining data quality. This is for engineers and architects building AI systems who need to manage large volumes of AI-specific data more efficiently.
Use this if you need to dramatically shrink the size of your AI embeddings or KV-cache tensors to save storage and improve performance within your AI infrastructure.
Not ideal if you're looking to summarize text or reduce the number of tokens in natural language processing tasks, as it focuses purely on numerical data compression.
Stars
17
Forks
1
Language
Python
License
Apache-2.0
Category
Last pushed
Apr 01, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/vector-db/mindtro/semafold"
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