Tag: llmops

A July 2026 field guide to choosing self-hosted open models by exact checkpoint, license, resident memory, usable context, workload quality, and production economics.

Reader outcome: Replaced best-model listicle logic with an auditable deployment envelope for selecting and validating small self-hosted models.

Why a $1.05 Cloud Run L4 hour can be cheaper or more expensive than hosted inference depending on utilization, throughput, fallback rate, and accepted business outcomes.

Reader outcome: Built a reproducible cost model for comparing Cloud Run GPU capacity with token-priced APIs using active hours, workload shape, fallback, and accepted-result economics.

Why adding a private open model beside hosted APIs fails unless routing, fallback, evaluation, and authority are designed as one production contract.

Reader outcome: Defined a hybrid inference contract that routes bounded work to a private model while preserving hosted-model fallbacks, deterministic validation, and human review.

Why LLM fine-tuning projects fail when teams jump to NeMo or Hugging Face training commands before deciding the model, data, evaluation, serving, and governance loop.

Reader outcome: Defined a fine-tuning operating loop that connects base-model choice, data curation, PEFT, evaluation, distributed training, serving, and governance into one repeatable release path.

A practical AI strategy framework with a worked example that connects business levers, data readiness, pilots, evaluation, governance, deployment, and operating metrics.

Reader outcome: Defined an end-to-end AI strategy playbook and worked example that ties data readiness, use-case selection, model development, governance, deployment, and operating ownership to measurable business outcomes.