What local means

A local-model setup can run inference on a machine or private infrastructure the team controls. Ollama documents a local API surface, while llama.cpp supplies an open inference implementation. LM Studio publishes an app privacy page describing its data practices. These are primary descriptions of products and projects, not independent measurements of model quality. Local execution can change where prompts and repository text travel, but integrations, telemetry, package downloads, and optional cloud features still need inspection. Use the exact configuration, not the word local, to describe data handling.

Measure the whole workflow

A model that is inexpensive per call may be costly if it needs many retries or substantial human correction. Conversely, a slower local response can be acceptable when work is batched and review is already the bottleneck. Compare task completion after acceptance, not tokens per second alone. Include setup, updates, hardware use, context window behavior, tool reliability, and the quality of patches on your repository. Avoid carrying a public benchmark score directly into a private-code decision; the local prompt, tools, and task distribution may be different.

Treat operations as part of the product

Someone must choose model versions, check licenses, patch runtimes, monitor memory and disk use, and recover from a failed update. A laptop experiment and an internal shared service have different isolation needs. If a local model calls remote search or sends a ticket through a connector, those actions still leave the machine. Document each network path and credential boundary. This recommendation is engineering analysis, not a claim that the cited tools provide or lack a particular enterprise control in every release.

Run a narrow comparison

Select a handful of representative tasks: explanation, a small patch, a repository-spanning edit, and a failure diagnosis. Keep the same acceptance rubric and record elapsed time, correction rounds, tool calls, and reviewer effort. Include a case where the correct answer is to stop because information is missing. If local inference satisfies the useful cases, choose it for that bounded workload and revisit as models and hardware change. If it fails, retain the evidence rather than replacing it with general claims about open or closed models. No such comparison was performed for this draft. Record the model and runtime versions used.

What to carry into the work

  • Map actual local and remote data paths.
  • Check model and runtime license terms for the chosen versions.
  • Compare accepted task outcomes and reviewer time.
  • Assign update, monitoring, and recovery ownership.
Evidence boundary: this is a sourced editorial guide, not a hands-on product evaluation. Recommendations are our engineering analysis. Product documentation describes intended behavior, not independent proof of reliability.

Sources & dates

  1. Ollama API introduction ↗Ollama · Undated source · Checked 16 Sept 2026
  2. llama.cpp repository ↗ggml-org · Undated source · Checked 16 Sept 2026
  3. LM Studio Privacy Policy ↗LM Studio · Undated source · Checked 16 Sept 2026

Unknown source dates stay undated. Preparation is not publication; no historical byline or interview is implied.