Mac mini craze for openclaw 🦞 tips best model per Memory

If you're wanting to go with a Mac mini here is a rule of thumb.

💻 Mac Mini RAM → Max Model Size

8 GB → fp16: ~1B | int8: ~2B | int4: ~4B

16 GB → fp16: ~2B | int8: ~3–4B | int4: ~6–8B

32 GB → fp16: ~4–7B | int8: ~8–10B | int4: ~15B

64 GB → fp16: ~8–13B | int8: ~15–20B | int4: ~25–30B

128 GB → fp16: ~20–30B | int8: ~40–50B | int4: ~60B

256 GB → fp16: ~40–70B | int8: ~70–100B | int4: ~100B+

512 GB+ → fp16: 70–100B+ | int8: 100B+ | int4: 100B+

so you see where this gets expensive real quick.

if you would rather build a setup with a GPU capable follow this guide.

6 GB → fp16: ~1–2B | int8: ~2–3B | int4: ~4–5B | Example GPUs: GTX 1660 Super, RTX 2060

8 GB → fp16: ~3–4B | int8: ~5–6B | int4: ~8B | Example GPUs: RTX 3060 12 GB (limited), RTX 3070 8 GB

12 GB → fp16: ~7–10B | int8: ~12–15B | int4: ~20B | Example GPUs: RTX 3080 12 GB, RTX 4070 Ti 12 GB

16 GB → fp16: ~10–13B | int8: ~15–20B | int4: ~25B | Example GPUs: RX 7900 XT/XTX 20 GB, RTX 3080 16 GB

24 GB → fp16: ~20–30B | int8: ~40–50B | int4: ~60B | Example GPUs: RTX 4090 24 GB, RTX A5000 24 GB

32–48 GB → fp16: ~30–40B | int8: ~50–70B | int4: ~80–100B |

Example GPUs: RTX A6000 48 GB, AMD Radeon Pro W6800 32 GB

let me know what else you guys want to know about openclaw or local LLM setups.