LocalLLaMA weighs 27B core model

The real answer here is that the best core model is the one that leaves room for the rest of the system. In agentic setups, a slightly smaller model that stays fast and predictable often beats a larger one that eats all the VRAM and context budget.

• –Multi-agent orchestration burns tokens quickly, so raw parameter count is only one piece of the puzzle.
• –27B-class models often hit a useful balance of capability, latency, and memory pressure on a single high-end GPU.
• –The most valuable trait for a core brain is consistency under tool use, not just benchmark bravado.
• –If the surrounding stack already handles memory and routing, model selection should optimize for headroom and throughput.
• –The thread reflects a broader local-LLM trend: practical system design matters as much as the model itself.