Musings on Semi-Local Inference

I recently threw out a random thought on Twitter, wondering if there might be room for something I called semi-local inference. This wouldn’t be on-device processing, but something like using a WiFi router to run powerful language models (LLMs). I was curious about the potential benefits—speed, cost, and privacy—over using APIs to power and control smart home or office devices. Here’s the tweet that started it all:

“Random thought, but curious if there’s room for semi-local inference (not on device, but for example, a wifi router that can run powerful LLMs). Speed, cost, privacy benefits over using APIs to power and control smart home/office devices, etc.”

The discussion that unfolded was a vibrant mix of insights, skepticism, and visionary ideas from tech enthusiasts and industry experts. It’s incredible how Twitter serves as a platform for such open, asynchronous brainstorming.

Weighing the Practicality and Economic Aspects

Robert Chandler (@bertie_ai) was quick to point out the cost issues associated with semi-local inference. He noted that the cost of hardware capable of running large models would be significant, especially since these transistors would often sit idle. However, I liked the idea of possibly selling back idle compute power to a marketplace—an appealing if complex solution.

Expanding on this economic angle, Moon (@spatialweeb) proposed that such devices could earn money by performing inference for others, hinting at a decentralized model. This suggestion highlights a potential financial incentive for individuals to invest in semi-local inference capabilities, aligning technological advancement with economic benefits.

Peter Downs (@peterldowns) shared his practical setup where he runs local models on his gaming computer, using Tailscale and LLM Studio. His experience shows that personal implementations of semi-local inference aren’t just theoretical but are already here for some of us.

Broad Applications and Emerging Challenges

In the commercial space, Tiny Corp and Truffles are exploring similar technologies. Tiny Corp has introduced an AI system aimed at democratizing high-performance AI capabilities at home, while Truffles is focused on an AI inference engine designed to run open-source models efficiently at home. These examples show active industry efforts to leverage semi-local inference technology.

Meanwhile, Enrico from Big-AGI (@enricoros) pointed out a significant hurdle: the lack of standards for handing off inference to an edge device, which includes challenges like discovery, update, versioning, and performance guarantees—crucial areas that need addressing for broader adoption.

The Edge and Beyond

Jorge Alcantara (@jorgeakairos) and others likened the idea to edge computing, where processing is done closer to data collection points rather than centralized data centers. This concept is crucial for applications requiring real-time processing and heightened privacy. Jon Radoff (@jradoff) emphasized that the real ‘edge’ might be as close as the devices in our own homes or offices.

Looking Ahead

The feedback and ideas from this conversation have been enlightening. While the path forward for semi-local inference involves significant challenges—like hardware investment and the need for new standards—the potential benefits in terms of responsiveness, privacy, and cost are compelling.

This discussion on Twitter has been a fantastic journey of collective intelligence and creativity. As we continue to explore the possibilities, the insights from the community are invaluable in shaping the future of how we interact with smart devices and the broader Internet of Things.






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