PRISM-DQ simplifies LLM quantization, drops calibration

PRISM-DQ represents a structural shift from static quantization to dynamic, importance-based compression for the local LLM ecosystem.

• –Dynamic bit allocation (2-bit to 4-bit) preserves reasoning capabilities by protecting high-impact weights identified via spectral analysis.
• –Eliminating calibration datasets removes the data-prep bottleneck, allowing users to quantize any model instantly.
• –Native GGUF support provides a "drop-in" upgrade for popular loaders like Ollama, LM Studio, and llama.cpp.
• –The accompanying 1-bit Bonsai model series demonstrates extreme efficiency, running 8B models on standard smartphones.
• –Backing from Khosla Ventures and Caltech lineage validates "intelligence density" as the new benchmark for model performance.