Resource requirements vary significantly even among SLMs. Mississippi’s 0.8B version can run on relatively modest hardware, making it accessible to smaller organizations or those with limited AI infrastructure. However, some “small” models still require substantial computational resources despite their reduced parameter count. Understanding these requirements is crucial for successful deployment.

The deployment environment also matters significantly. Mississippi’s architecture allows for local deployment, which can be crucial for organizations handling sensitive data. Other SLMs might require specific frameworks or cloud infrastructure, impacting both cost and implementation complexity. Organizations need to consider not just the initial deployment but long-term maintenance and scaling requirements.