Tagged: model selection
3 articles on model selection.
The Open-Weight LLM Landscape in 2026: What Engineers Actually Need to Know
The open-weight ecosystem has matured faster than most engineers realize. MoE proliferation, hybrid attention, and extended context windows are changing what's actually deployable on-premise — and that matters more than ever for healthcare AI.
When to Look Beyond Standard LLMs (And When to Stop Overthinking It)
Most teams should use a frontier API and move on. But there are specific situations — extreme latency, long-context scale, cost walls, privacy constraints — where alternative architectures actually matter. Here's the decision framework I use.
Trading Speed for Quality: A Practical Guide to Inference-Time Scaling
Inference-time scaling lets you tune the latency-quality tradeoff at runtime rather than at training time. Here is a practical framework for deciding when to use Best-of-N sampling, beam search, iterative refinement, or one-shot generation — with real examples from clinical AI.