Decentralized Hybrid LLM Inference Architectures Under Free-Tier Infrastructure Constraints

Access to advanced artificial intelligence systems is increasingly shaped by infrastructure and cost rather than capability alone. While large proprietary models dominate public benchmarks, a growing ecosystem of open-weight models offers an alternative path that prioritizes local control, transparency, and privacy. This paper examines whether such models can be deployed and used meaningfully under free-tier computational constraints. Rather than proposing new algorithms, this work focuses on system-level analysis and hands-on deployment. Open-weight reasoning models were examined in terms of memory requirements, inference latency, privacy properties, and operational stability when run on single-GPU free-tier instances such as NVIDIA T4 and P100. Particular attention is given to the gap between benchmark-reported performance and what is practically achievable on constrained hardware. The analysis highlights a clear hardware capability gap: while large distilled reasoning models (e.g., 32B variants) report strong benchmark results, free-tier infrastructure realistically supports only smaller 7B–8B deployments without aggressive quantization and offloading. Experimental observations confirm that these smaller models remain usable for reasoning-oriented tasks, albeit with longer setup times, variable latency, and limited throughput. The findings suggest that open-weight models can function as privacy-preserving reasoning systems for specific use cases, but they do not replace hosted platforms universally. Instead, they occupy a distinct role shaped by accessibility, user control, and infrastructure limits. A full reference implementation, execution notebook, and empirical runtime evidence are publicly available to support reproducibility.