i8 vector quantization: how it works and when to use it
i8 vector quantization, stores vectors in int8 for about 4x less memory, so more of your working set fits in RAM. Here's how Crowkis does it and why it matters for cost and safety.
Every reworded repeat of a question you've already answered is a full-price model call. i8 vector quantization is how Crowkis stores vectors in int8 for about 4x less memory, so more of your working set fits in RAM.
How it works
Crowkis stores vectors in int8 for about 4x less memory, so more of your working set fits in RAM. It runs inside one Redis-compatible engine, so it composes with semantic caching, agent memory, and the other intelligence layers instead of being a separate service you wire together.
Why it matters
Repetitive LLM workloads are where the money is, and semantic caching can cut costs up to 60-70% on repetitive workloads. i8 vector quantization is part of what makes that reuse safe rather than reckless, the difference between a cache you trust in production and one you audit after every incident. Runs self-hosted with zero egress, nothing leaves your machine.
Infrastructure earns the critical path one boring, verifiable feature at a time.