Learn the controls and knobs

Primary knobs for LLM serving: precision/quantization, max batch tokens, max context, concurrency, KV budget, admission control, timeouts.

Write a sheet for the multi-tenant chat API with columns: control, current value, predicted benefit, predicted cost, rollback trigger. Fill it using this topic’s real tension: Larger batches improve GPU utilization and tokens per second but can worsen queueing and per-user latency. Quantization reduces memory and may increase throughput with possible quality loss. Longer contexts expand usefulness while sharply increasing KV memory and prefill work.

Change one LLM serving family at a time. If you move two knobs and the multi-tenant chat API improves, you learned a cocktail, not a cause—and you cannot roll back surgically.

End users of the multi-tenant chat API should see only safe dials related to LLM serving. Infrastructure limits, private prompts, and policy thresholds stay server-owned. A user-facing control that bypasses those limits is a vulnerability dressed as UX for LLM serving.

Publish the LLM serving control sheet next to the multi-tenant chat API runbook. On-call should see which knob moved in the last deploy without reading chat archaeology. Unknown LLM serving knobs are unowned knobs.

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 / controls-and-knobs.

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 / controls-and-knobs.

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.

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.