Local models went from mostly useless to actually useful really fast. What changed?

Reddit r/LocalLLaMA News

Summary

The post notes that local AI models have become significantly more useful over the past year, moving from toys to practical tools for coding and workflows, despite still lagging behind closed models for complex tasks.

https://preview.redd.it/knc4ht7bft7h1.png?width=1048&format=png&auto=webp&s=49abdb8b0f358e799ecb06aa49134d9b0fd49336 Mitchell Hashimoto had a good point earlier: local models went from basically useless to actually useful in what feels like one year. I think thats pretty accurate. A year ago I mostly treated local models like toys for privacy, simple chat, or small RAG tasks. Now people are actually using Gemma, Qwen, GLM, Kimi, etc. for coding, private docs, local workflows and even replacing some API calls. I dont think they fully replace the best closed models for long repo work yet. The gap is still obvious when the task needs planning, context, and fixing its own mistakes. But the jump in usable quality feels real. For people running local models every day, what changed the most for you? Better base models, better quants, better tools like llama.cpp/Ollama, more VRAM or something else?
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