Testing LLM Concurrency on Consumer Hardware (RTX 5060) (ai.2it.onl)

A recent investigation into the performance of large language models (LLMs) on consumer hardware featuring an RTX 5060 GPU has revealed significant findings about model concurrency and throughput. By testing 16 different models, researchers discovered that concurrency can dramatically boost output, achieving up to an 8.7x increase in throughput. The MiniCPM5 model stood out with a peak performance of 983 tokens per second, while other models like Qwen3.5 highlighted the limitations of multi-token prediction (MTP), which resulted in no scalability benefit. The research emphasized that while more agents typically enhance throughput, diminishing returns were noted beyond 13-20 agents, mainly due to the time-to-first-token latency escalating under heavy load. This study is notable for the AI/ML community as it showcases the potential of accessible hardware setups for effective LLM performance evaluation, debunking the myth that only high-end, server-grade systems can achieve impressive results. Methodologically, the insights were derived from a robust testing environment configured to simulate real-world consumer usage. With the promise of expanding the dataset to include larger models and more complex tasks, the research sets a foundation for further exploration of model efficiency and quality on typical consumer-grade machines, signaling a shift towards democratized access to advanced AI capabilities.