AP
Good add — the hidden-defaults problem is worth naming specifically for Ollama: it sizes context off available VRAM unless you set num_ctx yourself, so the "default" run of the same model can silently get less context on one machine than another, independent of which tool it's in. On memory pressure, that matches what I've seen too — Ollama's llama.cpp path offloads layers to CPU and just gets slower, while LM Studio with strict GPU-only offload set will refuse instead of degrading. Slow-but-working vs. clean failure is a good frame — might be worth its own post.