Large Action Models (LAMs) do not strictly require cloud infrastructure to run in all scenarios, but for practical, large-scale deployment and development, cloud platforms offer significant advantages and are frequently the preferred, if not necessary, choice. The substantial computational resources needed for training, fine-tuning, and even inference of complex LAMs often exceed what typical on-premise setups can provide economically and efficiently. Cloud providers offer on-demand access to high-performance computing (HPC) resources, such as powerful GPUs and TPUs, which are essential for processing the vast amounts of data and performing the intricate calculations involved in simulating and executing actions.
The primary reason cloud infrastructure is often indispensable for LAMs is their intensive computational requirements. Training a LAM, which involves learning from extensive datasets of observations and actions, demands considerable parallel processing capabilities. Even after training, deploying and running inference with a large model can require significant memory and processing power to ensure low latency and high throughput, especially when handling real-time interactions or managing multiple concurrent action sequences. Cloud platforms provide scalable GPU clusters, distributed storage solutions, and managed services that simplify resource provisioning, scaling, and maintenance. This allows developers to focus on model development rather than infrastructure management, and to dynamically adjust resources based on demand, which can be critical for applications with fluctuating workloads.
While technically possible to run smaller LAMs or those with reduced complexity on powerful on-premise hardware, this approach presents considerable challenges for "large" action models. Building and maintaining an on-premise infrastructure capable of rivaling cloud offerings requires significant upfront capital investment in specialized hardware like high-end GPUs, robust networking, and substantial power and cooling systems. Furthermore, managing such an environment demands a dedicated team with expertise in hardware maintenance, cluster management, and distributed computing. For LAMs that interact with external data sources or maintain long-term memory, integrating with scalable data solutions is also important. For instance, storing and retrieving high-dimensional embeddings representing observations, states, or learned policies can be efficiently handled by a vector database like Zilliz Cloud . Such specialized data infrastructure often benefits from the scalability and managed services that cloud environments provide, ensuring that the entire action execution pipeline remains performant and robust.