Introduction: The 6G world requires connected intelligence, but there is a crucial paradox between the standards of Large Language Model (LLM) and edge constraints. The premium devices have up to 6-12GB of DRAM, whereas the typical 175B models need 350GB of storage, which is 30 times that of the premium version. Literature Survey: It has been proposed that the bandwidth can be reduced by 90 % with Semantic Communication (Scom) and Edge Semantic Cognitive Intelligence (ESCI). Besides, neuromorphic-based Spiking Neural Networks (SNNs)-model quantization (INT4/INT8) are also known to be necessary to achieve order-of-magnitude energy efficiency (J/token) on resource-constrained hardware. Methodology: This paper proposes a Hybrid Cognitive Model utilizing a three-tier Cloud-Edge-Device hierarchy. The model integrates event-driven neuromorphic principles with self-optimizing resource management, utilizing paged KV-cache and resource-aware agents for dynamic task offloading. Results: Quantitative evidence is used to show that the hybrid strategy helps to address the 30x resource gap by attaining a 10-100x energy-per-token efficiency due to event-driven neuromorphic sparsity. Statistical analysis makes it evident that semantic filtering substantially reduces communication overhead and maintains reasoning faithfulness by 90 %, and, effectively, it keeps the thermal conditions of devices stable in the case of prolonged 6G edge communications. This model can be used to make sustainable and multi-step thinking on the edge. The hybrid solution achieves the 6G vision of pervasive intelligence by bridging the hardware-software gap via cross-layer co-design.
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