MightyRayn: Algorithmic Model Compression for Million-Token Context on Consumer Hardware

We present MightyRayn, a 5-axis compression runtime that achieves 1,028,312 token effective context on a consumer laptop equipped with an Intel i5-8365U processor, 16 GB of RAM, and no GPU, using a 9B parameter model quantized to 4-bit. The system combines five orthogonal compression strategies: (1) progressive skill withdrawal inspired by SKILL0, which removes inference scaffolding as the model demonstrates competence; (2) inference-time bidirectional budget enforcement inspired by BCR, applied without any retraining; (3) KV cache-aware context compression that eliminates redundant key-value entries across attention layers; (4) streaming extractive memory with a 161.2x compression ratio that distills ingested corpora into retrievable memory entries; and (5) diffusion-guided layer-skip compression, a novel runtime framework that applies VQ-fingerprinted input classification to predict and skip inactive transformer layers. The pipeline achieves end-to-end compression of 6,121x — reducing 1,028,312 ingested tokens to a 168-token retrieval query — with successful needle-in-haystack retrieval at 75% corpus depth in 42.2 seconds. BCR bidirectional budget control achieves 91.4% output token reduction (2,048 to 176 tokens) while automatically expanding when quality degrades below a threshold. KV cache quantization reduces attention memory by 72% and improves inference speed by 19% on an 8.2B parameter model. All components are implemented in pure Python with zero external dependencies. These results challenge the prevailing assumption that million-token context requires datacenter-class GPU infrastructure.