GPT-OSS 120B still anchors 60GB local model discussion.

This is less a product announcement than a useful snapshot of where the local-model sweet spot still is for prosumer hardware: very large quantized models remain viable, but reliability matters more than raw parameter count.

• –The post highlights a practical ceiling for laptop-class local inference: 60GB-ish total memory can stretch to 100B+ models, but only with aggressive quantization and tradeoffs.
• –GPT-OSS 120B is framed as the current quality baseline because it remains predictable enough to trust for assistant-style background tasks.
• –Qwen 3.5 122B loses ground here not on size but on perceived hallucination and inconsistency, which is exactly what kills second-brain workflows.
• –The Nemotron Super 49B suggestion shows the community bias toward smaller models that may give up benchmark bragging rights but win on stability, language support, and fit.
• –For AI developers, the thread is a reminder that local deployment choices are still driven by quant quality, inference behavior, and hardware balance more than headline model size alone.