Despite the buzz around running local models on dedicated hardware like a Mac Studio, the most pragmatic first step is to use a cloud-based provider like Open Router. This allows you to access and experiment with models like GLM 5.2 immediately without a large, upfront capital expenditure on equipment.

A Stanford study found that the vast majority of queries sent to powerful frontier models don't require their advanced capabilities. These tasks could be handled by smaller, faster, and more private local models at virtually no cost, revealing a massive inefficiency in the current API-centric approach.

Model leaderboards are misleading. To ensure a consistent user experience, companies must develop their own evaluation suites reflecting their specific workloads. This allows them to swap underlying models for cost or capability reasons with confidence that the customer-facing outcome remains reliable and high-quality.

Constructing a robust eval set involves a process akin to binary search. First, establish a performance floor with an easy, canonical task to ensure basic competency. Then, establish a ceiling with a frontier-level hard task. This maps the model's capabilities and helps fill in the gaps with medium-difficulty prompts.

PMs often default to the most powerful, expensive models. However, comprehensive evaluations can prove that a significantly cheaper or smaller model can achieve the desired quality for a specific task, drastically reducing operational costs. The evals provide the confidence to make this trade-off.

Instead of asking if a local model is as powerful as a frontier cloud model, determine if it's sufficient for the specific task. Local AI's advantages in privacy, latency, and cost can make a product better even with a 'less smart' model.

Relying solely on premium models like Claude Opus can lead to unsustainable API costs ($1M/year projected). The solution is a hybrid approach: use powerful cloud models for complex tasks and cheaper, locally-hosted open-source models for routine operations.

If all your evals pass, you don't know the current limits of your system. Evals that consistently fail act as a benchmark. When a new foundation model is released, rerunning these tests immediately reveals if it has overcome previous limitations.

A powerful AI workflow involves using cheap, 24/7 local models for high-volume, initial-pass tasks like finding potential security issues. These 'qualified leads' are then batched and sent to a powerful frontier model like Claude for the final, high-quality analysis.