Benchmark evaluation demonstrates 384-fold attention tensor compression in language models, suggesting a resolution to high-bandwidth memory bottlenecks.
Key Points
To resolve the off-chip memory bandwidth bottleneck in long-context large language model inference using a unified latent-state memory architecture.
Combined finite scalar quantization via the Generative Latent Reconstruction Protocol (GLRP v2.0) with Aegis-KV dynamic routing firmware.
Quantized 3072-float attention weight tensors into 16 discrete integers using a 4x4 discrete latent grid on real-world GPT-2 attention weights deployed on standard CUDA hardware.
Achieved a 384x physical byte-level compression ratio, reducing memory footprint from 48.00 MB to 0.1250 MB per 4096-token block.
Maintained a 94.15% mean cosine similarity of core semantic geometry while transforming attention scaling complexity from O(N²) to O(N) linear scaling.
Demonstrated an end-to-end transduction pipeline latency of 19.8 ms on legacy T4 silicon.
Cite This Study
Dr Sharanagouda N Patil (2026) studied this question.