Get The Ornith 1.0 Local Setup Cheat Sheet

Run a frontier-class coding AI on your own laptop — free, private, 100% offline. Ornith 1.0 (DeepReinforce, MIT-licensed, open source) is an agentic coding model that writes its OWN scaffold during training; the flagship beats Claude Opus 4.7 on SWE-Bench Verified (82.4) and Terminal-Bench 2.1 (77.5), trailing only Opus 4.8. This cheat sheet gets you running in minutes. The three numbers that decide everything: (1) MEMORY CEILING — your GPU VRAM, or unified RAM on Apple Silicon, the hard cap on model size; (2) QUANTIZATION — Q4_K_M (smaller, faster) vs Q5_K_M (a bit larger, higher quality); (3) CONTEXT WINDOW — how much it reads at once (up to 262K). A pick-your-model-by-memory table: 9B Dense for 8–16GB VRAM (laptops & small GPUs), 31B Dense for a bigger card, the 35B MoE at Q5_K_M for a 24GB GPU (~3B active/token — fast AND smart, the sweet spot for ~95% of people, the one builder Alex Finn called the best local model that doesn't need 200GB+ of RAM), and the 397B MoE flagship for 8×80GB rigs. Then the exact setup three ways: the one-command Ollama install (ollama run hf.co/deepreinforce-ai/Ornith-1.0-9B-GGUF — no Python, no GPU config, no API keys), the click-only LM Studio path, and power-user vLLM + llama.cpp serve commands. Plus first-task prompts to put it to work, the benchmark receipts, and honest caveats (small ≠ flagship; quantization is a trade; Apple Silicon RAM is your ceiling). Run a frontier model on your own machine — for $0.

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