Serving Large Language Models

ModelAsServiceSim treated the model as a service under load: queues, latency, and rate limits. Serving is inference at scale — reliability, not a single chat demo.

A simple serving path looks like this:

1. Route the request to an available model server
2. Tokenize the prompt into model-readable pieces
3. Batch compatible requests so the accelerator stays busy
4. Run inference and stream generated tokens back
5. Measure latency, errors, and resource use

The first token can feel slow because the model must read the whole prompt first. After that, tokens arrive one by one. Teams often track time to first token separately from total response time.

Large models need lots of accelerator memory. Quantization, model sharding, caching, and smaller specialist models can lower cost, but each choice may trade away quality or flexibility.

• Launching a chat feature for many simultaneous users
• Deciding between a hosted model API and your own infrastructure
• Explaining why a long prompt costs more and starts responding more slowly
• Planning capacity for traffic spikes

More requests do not automatically mean more copies of the model. Smart batching improves throughput, but waiting too long to fill a batch hurts latency. Serving is a balancing act between speed, cost, and capacity.

Also, a healthy server is not the same as a good answer. Infrastructure metrics and answer-quality evals measure different failures.

Use the Try yourself playground above first. Switch between training and inference, then name which parts of a live chat belong to serving.

Imagine 100 people prompt the same model at once. Write one sentence about what you would optimize first: latency, throughput, cost, or quality.