HyperMesh-512: Hyper-Parallel Inference Acceleration Architecture for Massive Language Models Based on a 2D Mesh NoC and a Decentralized 32-Channel LPDDR4X/DDR4 Bus

HyperMesh-512 is a hyper-parallel PCIe inference accelerator architecture engineered to overcome the Memory Wall bottleneck during autoregressive decoding of massive language models (such as Llama 3.1 70B/405B and DeepSeek-R1/V3 671B MoE). Featuring 512 GB of commodity LPDDR4X/DDR4 memory distributed across 32 independent physical 64-bit point-to-point channels coupled to 16 ASIC micro-chiplets in a 2D Mesh NoC (X-Y DOR routing), the board delivers 819.2 GB/s to 1,092.1 GB/s of sustained memory bandwidth. To address ultra-long context windows (≥ 128k tokens), each chiplet integrates a dedicated single-cycle affine INT4 to INT16 hardware decompressor, reducing 128k KV-cache footprint from 41.94 GB down to 10.49 GB and raising effective context read bandwidth to 3,276.8 GB/s equivalent. Audited Industrial BOM: US$ 1,680.00. Target MSRP: US$ 4,999.00 delivering 102.42 GB/k$ (a 48x capital-efficiency advantage over corporate HGX H100 clusters), with a 98.33% CapEx reduction and 96.57% electrical OpEx reduction for on-premise, air-gapped private generative AI workloads. Declaração de Natureza Teórica: Artigo teórico / preprint técnico contendo modelagem arquitetural, RTL sintetizável e projeções tecno-econômicas. Propriedade Intelectual e Licenciamento (Royalties): Titularidade de Tiago Barbosa Dias Maciel (CPF 060.184.906-09, [email protected]). Qualquer exploração comercial sujeita a licenciamento e royalty de US$ 50,00 por placa ou US$ 3,00 por chiplet integrado.