Memory Optimization Python Packages

tqai

TurboQuant KV cache compression for local LLM inference — 80% memory savings, near-zero quality loss on 8B+ models. PyTorch + MLX (Apple Silicon). Based on arXiv:2504.19874 (Google Research, ICLR 2026).

mlx-flash

Run AI models too large for your Mac's memory — at near-full speed. Intelligent expert caching, speculative execution, and 15+ research techniques for MoE inference on Apple Silicon.

diet-pandas

Automated memory optimization for Pandas DataFrames. Reduces memory by 50-80% and loads CSV 5-10x faster. Drop-in replacement that returns standard DataFrames - works with sklearn, matplotlib, and all your favorite libraries. Safe, fast, and zero refactoring required.

dfreduce

Automatically reduce the memory size of any pandas dataframe based on downcasting bit types efficiently

langchain-turboquant

LangChain VectorStore with TurboQuant compression (ICLR 2026) - 6x memory reduction, training-free, no GPU required. The first LangChain integration for Google Research's TurboQuant algorithm.

superstring

A fast and memory-optimized string library for heavy-text manipulation