In the Model Context Protocol (MCP) ecosystem, hosts, clients, and servers are distinct components that work together to enable distributed machine learning workflows. A host is a node that provides computational resources and manages the execution of machine learning models. A client is an application or user that submits requests (e.g., inference tasks or training jobs) to the system. A server acts as an intermediary, coordinating communication between clients and hosts while managing tasks like load balancing, authentication, or result aggregation. These roles are designed to decouple responsibilities, ensuring scalability and flexibility in distributed environments.
Hosts are responsible for executing models and processing data. They are typically equipped with hardware accelerators (GPUs/TPUs) to handle compute-heavy tasks efficiently. For example, a host might load a pre-trained neural network and run inference on input data sent by a client. Hosts register their capabilities (e.g., supported frameworks like PyTorch or TensorFlow) with servers, allowing the system to route tasks appropriately. In a training scenario, a host could also participate in federated learning by updating model weights based on local data. Hosts are stateless in many implementations, meaning they don’t store client-specific data between requests, which simplifies scaling and fault tolerance.
Clients initiate interactions by submitting tasks to servers. A client could be a web application sending image data for classification, a IoT device requesting anomaly detection, or a batch job scheduler triggering a distributed training pipeline. Clients define parameters like model type, input format, and deadlines. For instance, a client might send a JSON payload containing sensor readings and specify that results must be returned within 500ms. Clients don’t need to know which host processes their request—this abstraction allows the system to dynamically allocate resources. Some clients also handle post-processing, such as filtering model outputs or aggregating results from multiple hosts.
Servers manage the workflow between clients and hosts. They handle task queuing, match clients with available hosts, and enforce policies like rate limits or retries. A server might use Kubernetes to scale host instances based on demand or implement caching for frequently requested models. For example, if a client requests a ResNet-50 inference task, the server could route it to a GPU-equipped host optimized for vision models. Servers also provide APIs for monitoring task status and collecting metrics (e.g., latency, error rates). In advanced setups, servers orchestrate multi-step pipelines, such as preprocessing data on one host, running inference on another, and logging results to a database—all within a single client request.