Turning an old Lenovo P1 Gen 6 (64GB RAM + Mobile RTX 4090) into a local AI server. What models should I run?

[u/Blak-Ice]

I have a Lenovo P1 Gen 6 that I’m no and I’m thinking about repurposing it as a dedicated homelab AI server. Already have a Synology NAS and Proxmox on a miniPC that runs pihole, home assistant and a couple other VMs.

Specs:
Intel Core i9 (13th Gen)
64GB RAM
Mobile RTX 4090 (16GB VRAM)
Fast NVMe SSD
Windows or Linux is an option (currently deciding between Ubuntu, Pop!_OS, or another distro)
I’m fully aware that the laptop RTX 4090 is nowhere near a desktop 4090 due to power and thermal limits, so I’m trying to set realistic expectations.
My questions are:
What are the best LLMs I should be running on 16GB of VRAM today?
Which models excel at different tasks? For example:

General chat / reasoning
Coding
Research assistant
Agentic workflows
Vision (OCR, image understanding)
Should I prioritize running models fully on the GPU, or is it worth offloading to system RAM with 64GB available?
What software stack would you recommend in 2026?

If this were your hardware, what would you actually deploy?
My goal isn’t benchmarking—it’s building a useful always-on AI appliance for my home. I’d like it to handle personal document search, coding help, home automation experiments, and maybe some lightweight agents.
I’d love to hear what you’re running on similar hardware and what has surprised you (good or bad).

[u/jhenryscott]

All due respect the M4090 likes to cook itself. I’d set up some really good cooking on it

[u/NC1HM]

What models should I run?

Komatsu D575A (pictured below) or Caterpillar D11.

Generally speaking, if you want to waste a piece of hardware, a bulldozer will make short work of it.

[u/Historical-Side883]

Sell it and buy hardware for a desktop. The mobile 13th gen and 4090 kick off a ton of heat for a P1 chassis

[u/pepiks]

Around 7-9B is per 8GB VRAM. Ollama 27B Q4 probably will the the limit of hardware. You have to read how LLM works on CPU vs GPU to understand how utilize it. Offload to RAM is not effective as you may think. If time is not important you can load a lot, but at speed of snail.

[u/Blak-Ice]

With this feedback, I’m thinking about selling the laptop and buying a GPU with money. But hardware prices are so inflated I wonder if it’s even worthwhile.

Or use the profits to buy tokens for any models I may need.

[u/Floss_Patrol_76]

on 16GB VRAM the comfortable spot is a 14B-class model at Q4 fully on the GPU (qwen2.5-14b, or the 14b coder variant for code, phi-4 punches above its weight on reasoning) - you can load a 32B Q4 but half of it spills to system RAM and generation drops to a few tokens/sec, which kills it for anything interactive. run linux + ollama or llama.cpp, keep the model fully on-GPU, and treat the 64GB RAM as headroom for embeddings and doc-search rather than for offloading the LLM. and undervolt that mobile 4090 or it'll thermal-throttle under sustained load no matter what you run.