RIS-Kernel Enables Long-Context LLM Inference on Commodity Hardware

Large Language Models (LLMs) are typically limited in their ability to process very long documents due to the quadratic scaling of full self-attention, which demands extensive GPU resources. This research presents RIS-Kernel, an innovative, model-agnostic architecture called Reduced Interaction Sampling (RIS) that significantly mitigates this constraint. RIS transforms the self-attention mechanism from O(N^2) to a more efficient O(N log N) by employing sparse stochastic geometry, making long-context inference feasible on much more modest hardware. The effectiveness of RIS was validated on the Qwen2-1.5B-Instruct model, demonstrating its capability to handle contexts up to 65,536 tokens on commodity CPU servers with standard RAM, where dense attention would typically fail due to out-of-memory errors. RIS-Stochastic, even at low densities, achieved competitive or superior accuracy compared to native dense attention in controlled settings, and provided substantial retrieval gains in memory-constrained scenarios. This breakthrough suggests that advanced LLM capabilities for long document analysis can be democratized, moving beyond the exclusive need for expensive GPU clusters.