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 deployable on-premise. That matters more than ever for healthcare AI.
EngineeringRead more →
When to Look Beyond Standard LLMs (And When to Stop Overthinking It)
Most teams should use a frontier API and move on. There are specific situations where alternative architectures matter: extreme latency, long-context scale, cost walls, privacy constraints. The decision framework.
EngineeringRead more →
Trading Speed for Quality: A Practical Guide to Inference-Time Scaling
Inference-time scaling lets you tune the latency-quality tradeoff at runtime instead of at training time. When to use Best-of-N sampling, beam search, iterative refinement, or one-shot generation, with real examples from clinical AI.
EngineeringRead more →