Free Whitepaper

Local AI That Competes

Compare systems, not models. Whether a self-hosted open-weight model holds up is decided by the context you assemble, the tools it can call, the checks it has to pass and the training data your workflow produces on its own. Christoph Hess sets out five engineering levers and the conditions under which each one pays off.

  • Four conditions that decide whether a workload belongs on local inference at all
  • Why price per token is the wrong number, and what to measure instead
  • Five engineering levers, each with its own decision rule, its own failure modes and its own point of diminishing return
  • A 90-day pilot plan with explicit gates
  • Published evidence with the caveats left in, including the results that do not generalise beyond their own paper

Author

Christoph Hess

Christoph Hess

Head of Artificial Intelligence

What to expect

  1. Executive Summary
  2. 1. Start with the workload, not the model: the four conditions that make local AI a candidate
  3. 2. Why organizations choose local inference: control, governance, and the economics of sustained utilization
  4. 3. System quality can outweigh model size
  5. 4. Lever 1: Engineer the harness. Plan, act, verify, repair, escalate.
  6. 5. Lever 2: Specialize with PEFT and LoRA, including the variants worth trying when vanilla LoRA underperforms
  7. 6. Lever 3: Distill a stronger teacher
  8. 7. Lever 4: Learn from verifiable outcomes, using the CI pipelines and validators you already run
  9. 8. Lever 5: Consolidate stable prompts into weights
  10. 9. A practical maturity path: five stages, each with a gate in front of it
  11. 10. What not to localize
  12. Conclusion
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