Multi-Model Serving and Model Multiplexing

Serving multiple models on a shared fleet can reduce idle hardware when each model has modest or uneven traffic. A routing layer maps a request to a model identifier and sends it to a replica that can serve that model. In multiplexed designs, a worker may load a model or adapter on demand and cache it for later requests. Dedicated processes or GPUs can simplify isolation but waste capacity when traffic is sparse.

The resource constraint is not just the number of model files. Models may have different GPU memory footprints, tokenizer or preprocessing needs, startup costs, and runtime dependencies. Several resident models can compete for memory, while frequent evictions trigger repeated loading and cold latency. A model that fits alone may not fit alongside others or under a large batch. Measure GPU memory, load times, queueing, and inference latency with the actual model mix.

Routing policies can consider model identity, version, hardware compatibility, and traffic. Keep model metadata and artifacts versioned; never trust a raw client-supplied path. An allowlisted model key should resolve to a controlled artifact and matching preprocessing. Tenant boundaries matter: shared caches and logs should not expose another customer's data or weights. Isolate credentials, request state, and metrics appropriately.

Caching and eviction determine which models remain available. An LRU policy removes items that have been unused longest, but that may be poor when load cost, size, and next-request probability differ. Size-aware or cost-aware policies may work better, but add complexity. Track cache hit rate, eviction count, cold-start rate, and per-model utilization. Set limits so one popular model does not starve others.

Multi-model serving improves flexibility. Compare shared multiplexing with dedicated replicas or separate pools using representative demand patterns. Include model loading, preprocessing, routing, GPU contention, and failures in service objectives. Keep a rollback path for bad artifacts and test how the system behaves when a requested model is missing or cannot fit.