AINewsnow

Local AI Assistant vs Cloud AI: What I Learned Running LLMs on My Own Rig

As a developer who spends way too much time tweaking my local dev environment, I recently got obsessed with running AI models locally. Between Ollama, LM Studio, and open-weight models like Llama 3, the tech has come a long way. But it made me wonder: is going 100% local actually worth the hassle,…

Read the full story at DEV Community — Machine Learning ↗

Timeline · 1 report

  1. 2026-10-13 03:16 · DEV Community — Machine Learning
    Local AI Assistant vs Cloud AI: What I Learned Running LLMs on My Own Rig

More stories

  1. I clustered two old Android phones to run a 27B LLM locally. Bypassed RAM limits via C++ RPC split and Hadamard Ternary math (No overheating) — r/LocalLLM
  2. Ternary Bonsai 2 27B on a $250 Intel Arc B580: writes a working snake game at 108 t/s, 89 t/s at 115K context (open source) — r/LocalLLM
  3. llama.cpp optimizations for specific hardware and models — r/LocalLLaMA
  4. Qwen3.8-Flash-Next on an RTX 5090: 128GB RAM: IQ3_XXS vs IQ3_S vs UD-IQ4_XS — Speed and Accuracy with Strata — r/LocalLLM
  5. Benchmarked Strata 0.1.25 and 0.1.39 against llama.cpp on three cards: an RTX PRO 6000 (96 GB), a 3090 Ti and a 5090 laptop — r/LocalLLM
  6. Qwen 3.8 27B Q4_K_M running on a OnePlus 13 with 24 GB RAM — r/LocalLLM
  7. BrokenML: can agents fix broken ML jobs? — r/LocalLLaMA
  8. GLM-5.3-Flash (320B) on an M5 Ultra Mac Studio with llama.cpp: 61 tok/s, 262K context — r/LocalLLM

Get the daily brief of stories like this at 6:30 every morning →