Understand the mechanism

Load weights into GPU/CPU memory, batch prefill/decode, manage KV cache, admit/queue requests, stream tokens, apply auth quotas.

Read the LLM serving path as a pipeline for the multi-tenant chat API. At each stage, name the representation, the owner, and how information can be lost. Identifiers must mark prompt versions, model versions, indexes, and policies so “randomness” is not the default explanation for every bug in LLM serving.

approximate weight memory for 7B parameters at 16 bits = 7B × 2 bytes ≈ 14 GB, before KV cache and runtime overhead Scoped specifically to LLM serving / multi-tenant chat API / mechanism.

Keep the unit and the denominator visible when you discuss LLM serving. A percentage without a base, or a latency without a percentile, hides the failure mode this chapter cares about on the multi-tenant chat API.

Learned stages estimate; deterministic stages enforce. A fluent result from the multi-tenant chat API does not prove LLM serving used the right evidence. Preserve intermediates when privacy allows—candidate lists, traces, scores, citations—so you can see the first broken stage in the LLM serving path.

Operational correctness for LLM serving includes deadlines on the multi-tenant chat API. If stages that feed this workload sum past the user’s patience, trim earlier—usually pack less, retrieve less, or parallelize—before blaming the model vendor for LLM serving. Mechanism diagrams that ignore time are incomplete.

Also pin one numeric memory from this LLM serving chapter: approximate weight memory for 7B parameters at 16 bits = 7B × 2 bytes ≈ 14 GB, before KV cache and runtime overhead That number is not decoration; it is a template for how claims about LLM serving on the multi-tenant chat API should look in design docs. Scoped specifically to LLM serving / multi-tenant chat API / mechanism.

People confuse LLM serving with neighboring buzzwords when debugging the multi-tenant chat API. Before changing prompts, ask whether the broken stage was evidence gathering, the LLM serving judgment itself, validation, or the product action. Fixing the wrong stage creates folklore (“we tried LLM serving and it failed”) that blocks the next team on the multi-tenant chat API. Scoped specifically to LLM serving / multi-tenant chat API / mechanism.

Write a five-line artifact for this page: goal, inputs, check, owner, stop rule. Invent one fluent failure that the check would catch. Keep details specific to serving llms rather than generic AI advice.