Running large models for AI applications typically requires powerful and expensive hardware. For individuals or smaller organizations, this poses a significant barrier to entry. They often need help to afford the necessary top-tier GPUs to run models with billions of parameters, such as the latest iterations of Llama. This limits the accessibility and democratization of advanced AI technologies.
Currently, several solutions exist to address this issue. Cloud services provide access to powerful hardware for a fee, which can become costly over time and still leave users reliant on external providers. Additionally, there are techniques to optimize models to run on more modest hardware, but these often come with trade-offs in performance and accuracy.
A new solution, called Cake, aims to change this landscape. Cake is a Rust framework designed to distribute the computational load of large AI models running across a network of consumer devices. By leveraging hardware that might otherwise be considered obsolete, Cake turns various devices—such as smartphones, tablets, and laptops—into a heterogeneous computing cluster. This approach not only makes advanced AI more accessible but also offers a practical use for older technology, reducing electronic waste.
Cake works by splitting the computational tasks involved in running a model into smaller pieces that can be handled by different devices in the network. Each device processes a part of the model, combining the final results to produce the final output. This sharding process allows models that wouldn’t fit into the memory of a single GPU to be run across multiple devices. Cake batch tasks are done to minimize the delay caused by transferring data between devices and to ensure efficiency.
The effectiveness of Cake can be measured through several metrics. The framework supports various operating systems, including Linux, Windows, macOS, Android, and iOS, and can utilize different types of hardware acceleration like CUDA and Metal. This flexibility means that users can repurpose almost any device to contribute to the computational effort. Tests have shown that Cake can successfully run models with over 70 billion parameters by distributing the load across multiple devices, demonstrating significant potential in making large-scale AI more accessible.
In conclusion, Cake offers a promising solution to the problem of running large AI models without requiring expensive hardware. Distributing the workload across various consumer devices leverages otherwise obsolete technology to provide a cost-effective and environmentally friendly approach to advanced AI computation. While still experimental and subject to ongoing development, Cake represents a significant step towards democratizing AI and making it more accessible to a broader audience.
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