EdgeRazor: Mixed-Precision Distillation Framework for 1.88-bit LLMs
ttkciar · reddit · 2026-08-03
EdgeRazor is a lightweight framework for LLMs that uses mixed-precision quantization-aware distillation to preserve model competence under extremely low memory conditions.
- Core Mechanism: Employs an entropy-guided distillation process to translate the teacher model's logit probability distributions into the student model's low-bit/mixed-precision features without preserving original parameter structures.
- Performance: While computationally more expensive than standard quantization, it is significantly cheaper than Quantization-Aware Training (QAT) and maintains robust reasoning even at 1.88 bits per parameter.
- Engineering Friendly: Because it doesn't alter the internal representation, inference engines like llama.cpp can run it without code modifications.
The authors have open-sourced the code and uploaded demo models based on MobileLLM and Qwen3 to Hugging Face.
More from Infra
- Baidu open-sources FlashMask: up to 1.55x faster than FlashAttention-4 for long-context training — BaiduResearch · 2026-10-16
- GYRO quant of Qwen3.8-Flash-Next tested: near Q3 quality at much smaller size — PlasticRevenue4601 · 2026-10-16
- RL train-sample systems are frontier AI's hardest infra problem — and a trade secret — brianryhuang · 2026-10-16
- Local tuning of GLM-5.3-Flash on 2×GB10 lifts decode from 49 to 58 tok/s — EAccelerate_42 · 2026-10-16
- 3x DGX Spark cluster runs GLM-5.3-Flash locally behind a proxy, 1.75M tokens/day at 119W — Teknium · 2026-10-16
- Optuna creator Akiba: when 2 hyperparameters cost $20M, what's left for BO/HPO — Stefania_druga · 2026-10-16