Local LLMs & Ollama
Learn the controls and knobs
Each local LLMs control is a hypothesis about a metric under a workload—not a synonym for quality on the offline clinical note assistant.
1Learn the idea
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Control map
Primary knobs for local LLMs: model size, quantization, context length, GPU/CPU offload, update channel, disk encryption.
Write a sheet for the offline clinical note assistant with columns: control, current value, predicted benefit, predicted cost, rollback trigger. Fill it using this topic’s real tension: Locality helps privacy and offline use and shifts patching, capacity, and quality risk onto you. Stronger quantization saves RAM and can hurt medical nuance.
Change one local LLMs family at a time. If you move two knobs and the offline clinical note assistant improves, you learned a cocktail, not a cause—and you cannot roll back surgically.
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Product exposure
End users of the offline clinical note assistant should see only safe dials related to local LLMs. 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 local LLMs.
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Make it operational
Publish the local LLMs control sheet next to the offline clinical note assistant runbook. On-call should see which knob moved in the last deploy without reading chat archaeology. Unknown local LLMs knobs are unowned knobs.
Also pin one numeric memory from this local LLMs chapter: A 7B 4-bit model may fit in ~5–6 GB RAM yet lose accuracy on rare clinical abbreviations—measure on your notes, not only tokens/sec. That number is not decoration; it is a template for how claims about local LLMs on the offline clinical note assistant should look in design docs. Scoped specifically to local LLMs / offline clinical note assistant / controls-and-knobs.
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Common mix-ups
People confuse local LLMs with neighboring buzzwords when debugging the offline clinical note assistant. Before changing prompts, ask whether the broken stage was evidence gathering, the local LLMs judgment itself, validation, or the product action. Fixing the wrong stage creates folklore (“we tried local LLMs and it failed”) that blocks the next team on the offline clinical note assistant. Scoped specifically to local LLMs / offline clinical note assistant / controls-and-knobs.
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Rehearsal (local-llms/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 local llms rather than generic AI advice.
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Rehearsal (local-llms/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 local llms rather than generic AI advice.
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Before you start
Why this matters
From [model size, quantization, context length, GPU/CPU offload, update channel, disk encryption], pick one control for local LLMs on the offline clinical note assistant. Predict which metric rises and which cost rises if you increase it.
In the wild
See how this idea shows up as a product and a company — then come back to the lesson. Skills transfer across vendors.
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