ZeroMass Architecture: Mitigating the "Memory Wall" in LLM Inference via Asynchronous Decompression and Semantic Proxies

ZeroMass Architecture proposes a theoretical systems architecture for mitigating the Memory Wall in Large Language Model (LLM) inference on commodity hardware. Rather than treating model parameters as a static asset permanently resident in GPU memory, ZeroMass reformulates inference as a dynamic asynchronous streaming process. The architecture combines low-rank matrix decomposition (SVD), asynchronous memory pipelines, semantic context reduction through proxy graphs, and dynamic KV-cache recompression to reduce memory pressure while preserving computational efficiency. This work presents the mathematical framework, architectural design, and theoretical analysis supporting the proposed approach. It does not claim experimental validation; implementation of a production-grade runtime and empirical benchmarking are left for future work. The paper is intended as an open research contribution and is released under the Creative Commons Attribution 4.0 (CC BY 4.0) license to encourage discussion, experimentation, and further development by the research community.