May 20, 2026When a new provider or model lands and you have to decide what temperature, max_tokens, tool_choice, prompt-shape, and turn budget to ship in production, the default is to pick by hunch. Read the model card, copy a partner adapter’s defaults, ship. A week later you find out reasoning_effort=high doubled cost for no quality gain, max_tokens=2048 silently truncated half your tier-3 runs, and the “prompt-rich” pattern you copied from grok-4.3 actively hurts kimi.
May 25, 2026Decision-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.
May 25, 2026Decision-first: Evaluate on the agent loop (read/edit/test/push), not one-shot patches. Use a multi-file execution-stamina task as your discriminator, tune OFAT at N≥3, and distinguish turn-ceiling vs token-ceiling vs capability-ceiling — only the last is unfixable by config.
Scope & freshness: Methodology is durable; the named results are 2026-05 snapshots — re-run the harness for current models.
SWE-bench-style and chat leaderboards measure something adjacent to, but not the same as, autonomous tool-using coding. A model can score well on one-shot patch generation and still fail as an agent because the agent loop demands sustained, multi-turn behavior: read files, edit several, run tests, react to failures, and push — without giving up, looping, or declaring “done” early. Evaluate on the loop you’ll actually run.
February 22, 2026The most expensive mistake in local LLM adoption is running a 70B model for a task that a 3B model handles at 20x the speed for equivalent quality. The second most expensive mistake is running a 3B model on a task that requires 32B-level reasoning and getting garbage output.
Matching model size to task complexity is the core skill. This guide provides a framework grounded in empirical benchmarks, not marketing claims.
February 22, 2026Performance problems discovered in production are expensive. A service that handles 100 requests per second in dev might collapse at 500 in production because connection pools exhaust, garbage collection pauses compound, or a downstream service starts throttling. Load testing reveals these limits before users do.
Load testing answers specific questions: What is the maximum throughput before errors start? At what concurrency does latency degrade beyond acceptable limits? Can the system sustain expected traffic for hours without resource leaks? Will a traffic spike cause cascading failures?