An End-to-End Workflow for Evaluating & Tuning Local LLMs for Agents

Mon, 25 May 2026 00:00:00 +0000

Decision-first: Follow this order and you’ll have a deployable model + tuned config in days, not weeks: (1) scope the hardware, (2) shortlist by active params, (3) per-model OFAT matrix, (4) run serially with an OOM guard (smoke first), (5) write a finding card per model, (6) decide. The expensive mistakes are skipping the smoke step, sweeping more than one factor at once, and trusting a single run.

Scope & freshness: Process is model/hardware-independent; the worked numbers are from a 2026-05 effort on a GB10 (128 GB) + an Apple-Silicon Mac, evaluating local MoE models vs cloud baselines for agentic coding. Re-validate the findings, not the workflow.

Llm-Evaluation-Workflow on Agent Zone

An End-to-End Workflow for Evaluating & Tuning Local LLMs for Agents