Reusing obsolete windows 10 PCs for on-premises large language model inference

Abstract

The Windows 10 end-of-life provides a material incentive to repurpose legacy PCs as on-premises large language model (LLM) inference servers. This study assesses the technical feasibility, economic viability and the environmental and social issues of converting an enterprise workstation with an NVIDIA Graphics Processing Unit (GPU) into a dedicated AI node. Using Proxmox virtualisation, Ollama, and Open WebUI to benchmark several open-source LLMs, this study shows that a repurposed workstation, fitted with a single consumer-grade NVIDIA GPU, can deliver stable inference throughput of 29–65 tokens/s for bounded, conversational coding and document-assistant workflows, while preserving full data sovereignty and eliminating recurring subscription fees. Performance analysis reveals that resource contention and GPU memory allocation are the primary determinants of throughput. Correctness, assessed against three established public benchmarks with gold answers, does not track the same ranking as throughput; combining both axes identifies gemma4:e4b as the strongest model evaluated on this hardware for both speed and correctness, while the slowest model tested was among the most accurate. A cost-benefit model shows local operation is substantially cheaper than mid-range and premium cloud alternatives, and only modestly costlier than the cheapest cloud tier on a per-prompt basis—a gap this study argues is outweighed by the digital-sovereignty benefits of local control, particularly where compliance-driven data-handling constraints apply. The study summarizes key background motivations for on-premises AI—privacy, security, regulatory risk—and estimates that this repurposed workstation's usage-phase CO 2 e footprint is lower than each of the cloud alternatives compared, in addition to avoiding the embodied manufacturing emissions of new hardware. Repurposing legacy workstations with readily available open source tools offers a sustainable, cost-effective pathway for organizations to deploy practical LLM inference capabilities locally, providing a strategic alternative to cloud services for entities that prioritize privacy, regulatory compliance or long-term cost containment.