TurboQuant
Two-stage extreme quantisation algorithm — 3-bit zero-loss KV-cache compression with no training.
What it is
TurboQuant is a Compression product from Binary AI Labs. Two-stage extreme quantisation algorithm — 3-bit zero-loss KV-cache compression with no training.
Specs
- Category
- Compression
- Version
- Current release — versioned per engagement
- Deployment
- Library, run in your own training and inference pipelines
- Integrations
- PyTorch, Hugging Face
- Stack
- Triton · CUDA · PyTorch
- Pricing
- Per engagement — contact hello@binarylabz.com for a quote
- Support
- Named engineer, business-hours SLA; 24/7 by arrangement
- Compliance
- SOC 2 controls inherited from the host environment; data residency configurable
- PolarQuant random rotation
- QJL 1-bit residual correction
- Near-optimal distortion across bit-widths
- Deployable in real-time production
TurboQuant vs. building it yourself
The method is published; reproducing the tuned kernels and validating accuracy at target precision is the effort.
Questions
Does TurboQuant run on-prem?
Library, run in your own training and inference pipelines. Where the deployment target constrains the architecture, that is settled during the discovery sprint rather than after.
How is TurboQuant priced?
Per engagement rather than per seat, scoped from a discovery sprint. There is no public rate card because the variance between deployments is too wide for a headline number to be useful. Contact hello@binarylabz.com for a quote.
Should we build this ourselves instead?
The method is published; reproducing the tuned kernels and validating accuracy at target precision is the effort.