dblalock/bolt

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

2514 stars · 174 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Bolt is a C++ library (with a Python wrapper) for lossy compression of dense real-valued vectors that supports mathematical operations directly on compressed representations, offering 10-200x space and compute savings. It also includes MADDNESS (mithral), an approximate matrix multiplication algorithm aimed at accelerating neural network inference on CPUs.

Use cases

• compress large collections of dense vectors with minimal accuracy loss
• speed up dot products and distance computations on compressed vectors
• accelerate neural network inference on CPUs with approximate matrix multiplication
• reduce memory footprint of embedding or feature vectors
• research approximate matrix multiplication algorithms

When to choose

• you have mostly-dense vectors and can tolerate lossy compression
• CPU inference speed is a bottleneck and 10-200x approximation savings are acceptable
• you need theoretical error guarantees on approximations

When to avoid

• you need exact results
• you require GPU support or convolutions for MADDNESS
• you need a well-maintained Python wrapper, since it reportedly no longer builds for many users

Facets

library · maturity maintenance

machine-learning compression math benchmarking machine-learning databases performance cpp python cross-platform vector-compression approximate-matrix-multiplication maddness dense-vectors lossy-compression simd nearest-neighbor algorithms linux macos

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