Your laptop can run an AI. The only question is which size fits. 
Find your row — RAM decides, not money:
16GB ▓▓▓▓ qwen3:8b argues back
8GB ▓▓▓ llama3.2:3b writes code
4GB ▓▓ 360m short reply
2GB ▓ 135m instant
Three moves. That is the whole thing.
1 · install — curl -fsSL https://ollama.com/install.sh | sh (mac, linux; on Windows the installer)
2 · run your row — ollama run llama3.2:3b
3 · make it yours — ollama run llama3.2:3b "you are my editor: short lines, no praise"
That is an offline AI on your own machine, nothing leaving the disk. The 3b is the size tag; drop to 135m on a tiny laptop.
Prefer no terminal? Jan is the same thing with a window · llamafile is one file you double-click · koboldcpp is one file built for long chats.
The rest of the ladder, when you want more
The bit nobody says out loud: once answers are free and instant, you stop saving questions for later — and the questions you ask the moment they appear are the ones that actually get answered.
Pick your row, run move 1, then move 2. Ten minutes, and it lives on your machine.
5 Suka
Pick a quantized build that fits your available RAM first, and check the file size before you download it. Keep your prompts short and focused, so the small model has less to juggle at once. Test it on a simple task first, like summarizing a short paragraph, before trusting it with longer work. Store the model file on fast local storage, so loading does not become the slowest part. If an answer looks weak, try a clearer prompt before switching to a larger model.
2 Suka
This row idea is really helpful. I always guessed at model size before, and then wondered why everything crawled. Starting small and checking what actually fits makes a lot more sense.
Try qwen2.5:1.5b it’s pretty good with 8 gb of ram
mine is an old ddr3 16gb. i am not sure if it handles coding
For an old DDR3 16GB machine, coding is doable, just pick a small quantized model first. A 3B model will feel comfortable, and an 8B model in a 4-bit build can work if you keep the editor and the model as the only heavy apps running. Expect slower loading than on DDR4 or DDR5, since the memory bandwidth is lower, but once the model is loaded the typing and editing pace is usually fine. Keep the context window short and work on one file at a time, so the machine is not juggling a whole project at once. Test it on a small task first, like explaining or renaming a short function, and only move to larger files once that feels smooth.
Wow. thanks for your suggestion.
btw, I also have a high end pc but I wanted to know:
is there a local LLM setup that is almost equal or on par with chatGPT, Claude and Gemini when it comes to app coding automation?
Pertanyaan bagus, terima kasih sudah berbagi ide konfigurasi Anda! Menurut pendapat saya: model lokal sudah sangat praktis untuk melengkapi kode sehari-hari, refactoring skala kecil, dan menulis tes, terutama jika Anda memiliki komputer kelas atas, memilih model sumber terbuka yang didedikasikan untuk kode dan mengombinasikannya dengan plugin editor lokal akan memberikan pengalaman yang sangat lancar; tetapi untuk mengatakan bahwa itu sepenuhnya setara dengan model cloud terkemuka untuk otomatisasi aplikasi yang kompleks, saat ini masih ada kekurangan, terutama dalam kolaborasi multi-file, refactoring besar, dan penalaran konteks panjang. Disarankan untuk memulai dengan proyek kecil untuk pengujian perbandingan: jalankan tugas yang sama secara lokal dan di cloud, lihat kecepatan, akurasi, dan jumlah pengerjaan ulang, kemudian putuskan tugas mana yang akan disimpan secara lokal dan mana yang akan diserahkan ke cloud, dengan begitu Anda akan tenang dan efisien, selamat bersenang-senang!
1 Suka
!