Digital Event Horizon
MinionS is a revolutionary approach to distributing AI workloads, shifting from cloud-based frontier models to consumer devices, reducing costs while maintaining performance. This innovative method enables the collaboration between small on-device models and frontier models in the cloud, paving the way for powerful local applications and reduced reliance on expensive cloud APIs.
MinionS is a groundbreaking approach to distributing AI workloads, shifting large language model tasks from cloud-based models to consumer devices. The protocol enables collaboration between small on-device models and frontier models in the cloud, reducing cloud costs while maintaining performance. MinionS decomposes tasks into smaller subtasks, executes them locally, and aggregates outputs from remote LMs. The protocol introduces a decomposition-execution-aggregation loop to address weaknesses of small LMs and make them work harder and smarter. MinionS delivers 97.9% accuracy while costing only 17.5% compared to remote-only solutions in cost-accuracy tradeoff analysis.
MinionS is a groundbreaking approach to distributing AI workloads, shifting a substantial portion of large language model (LLM) tasks from cloud-based frontier models to consumer devices. This innovative method, pioneered by researchers at Together AI, enables the collaboration between small on-device models and frontier models in the cloud, reducing cloud costs while maintaining performance.
The concept of MinionS is rooted in the idea that consumer devices are equipped with increasingly powerful hardware, capable of running sophisticated small LMs. However, these devices currently underutilize their compute resources due to limitations in data processing and model optimization. The MinionS protocol addresses this by decomposing tasks into smaller subtasks, executing them on chunks of context locally, and aggregating the outputs from the remote LM.
The initial version of MinionS, dubbed "Minion," was tested with a simple approach: allowing the on-device LM to engage in a chat-like conversation with the cloud model. While this proved effective in reducing cloud costs, it suffered from limitations such as performance degradation due to long context struggles and multi-step confusion. The researchers behind MinionS recognized these challenges and set out to develop an enhanced protocol, which they have dubbed "MinionS."
MinionS introduces a decomposition-execution-aggregation loop that addresses the weaknesses of small LMs while making them work harder and smarter. The remote LM decomposes tasks into smaller subtasks, generates code for task decomposition and chunking on-device, executes subtasks in parallel, filters outputs, and communicates them to the remote LM. The remote LM combines local outputs, finalizes answers, or requests another round of subtasks.
The results of MinionS are nothing short of remarkable. In a cost-accuracy tradeoff analysis, MinionS delivered 97.9% of the accuracy of remote-only solutions while costing just 17.5%. This represents a significant breakthrough in reducing cloud costs without compromising performance.
To further optimize the MinionS protocol, researchers explored various levers to navigate the cost-accuracy tradeoff. Model choice played a crucial role, with local models above 3B parameters proving essential for the Minion/Minions protocols. Scaling of inference-time compute was also optimized through techniques such as repeated sampling, finer-grained decomposition, and context chunking.
The ultimate goal of MinionS is to establish a communication protocol between small on-device LMs and frontier LMs, reducing reliance on expensive cloud APIs while unlocking powerful local applications. As GPUs become ubiquitous across consumer and edge devices, this vision becomes increasingly plausible.
For practitioners, researchers, and hackers, the MinionS community invites collaboration and participation. The quickstart guide in the GitHub repository provides a straightforward entry point for applying MinionS to workloads, while further research opportunities lie in improving communication efficiency through co-design of local and remote models, alternative communication signals, and enhancing inference speed via systems and algorithmic advances.
In conclusion, MinionS represents a significant leap forward in AI workloads distribution. By harnessing the power of consumer devices and collaborating between small LMs and frontier models, researchers at Together AI have developed an innovative solution that reduces cloud costs while maintaining performance. As this technology continues to evolve, we can expect to see exciting applications across various domains, from continuous grunt work on device to intelligent, "always-on" applications.
Related Information:
https://www.digitaleventhorizon.com/articles/The-Rise-of-MinionS-A-Revolution-in-AI-Workloads-Distribution-deh.shtml
https://www.together.ai/blog/minions
https://www.financialsense.com/blog/21111/ai-productivity-boom-embracing-shift-toward-smaller-specialized-models
https://arxiv.org/html/2411.03350v2
Published: Tue Feb 25 10:32:27 2025 by llama3.2 3B Q4_K_M
