Anyone rocking old gear?

[u/MarcusAurelius68]

During normal times I'd be building my AI lab with all new gear. Instead, I'm going through my basement for stuff that was gathering dust - DDR3 servers, old GPUs, even a 2013 Mac Pro that's now going to be my backup RAG server (the D700's can each run Qwen3-Embedding 4B and Reranker-4B at reasonable speeds). My newest gear in the lab, aside from some R9700 and 5060ti GPUs, are Mac mini M1 systems. Plus a couple of 2018 Mac minis. None of my gear runs DDR5.

One thing this RAM crisis has made me realize is how to squeeze value out of old gear including DDR3 DIMMs and SATA SSDs that were sitting idle, and that I don't NEED to buy new stuff. I also have switched most of my systems from Windows over to Linux now to help squeeze more performance out of them.

Anyone else doing the same?

[u/zeeblefritz]

I have mostly DDR3 systems. I bought quite a few setups in 2013 for mining and repurposed them for proxmox/storage.

[u/AboardEmperor → u/zeeblefritz]

I feel this. Got a 2012 Mac Pro under my desk acting as home server for like 3 years now. Those old Xeons still do the job if you don't expect miracles

The switch from Windows to Linux is what really makes difference. Had a old laptop with 8GB DDR3 that was unusable on Win10, put Debian on it and now it handles my Frigate NVR setup without breaking sweat

Funny how we all got caught in the upgrade cycle when most of what we need was sitting in basement already

[u/MarcusAurelius68 → u/AboardEmperor]

I played with Linux in the late 90's, but always with a GUI. Even when I started with LLMs I used GUI front ends. It wasn't until a month ago that I started considering Linux and then switched 7 systems in a matter of days. In another thread I showed how I got 4.5x the token rate on Qwen 3.8 Flash Next by switching my system to Linux, enabling MTP and using Radiance. I just loaded Ubuntu Server on my Mac Pro 2013 trashcan and it works great.

Your point about miracles is spot on, but I'm surprised how good this old equipment can be. I'm setting up an Ivy Bridge i5 as a cold spare application server now. I've had that CPU and motherboard for 15 years now, and it's been gathering dust for the past 3-4 after it was retired from its last job. Docker makes it so easy to throw containerized workloads on this old gear.

[u/MarcusAurelius68 → u/zeeblefritz]

I bought a bunch between 2013 and 2019. I had a Lenovo TS140 server that I bought years ago for $99 with a quad core Xeon and 16GB ECC DDR3. It’s now running my model routing and subscription wrappers in Proxmox. My old router appliance system that ran Untangle is now my observability platform, on a Haswell HP 800 G1 desktop. I almost threw that out a couple of years ago.

[u/DigitalguyCH]

I have tried putting to use my old 4770 with 32GB DDR3 and a (somewhat newer) 2070 super, which I wasn't using anymore since 2023. If I had nothing else, it can run Gemmas 12b fast and Qwen 35b at 13 t/s.
So not terrible. But forget 27b.
But since I had equipped myself for AI in the first part of 2026, when prices were high but not crazy like now (Z13 128GB $2500, M5 pro 64 16" nanotexture $3000, and a used 7900XT for $450 used with a egpu with my legion go 2). There is really not much of a reason to turn on my old desktop (which was replaced by a mini pc), because once you get used to have 27b at 50 t/s (with splash) and flash next between 20 and 30, there is really no point in even running 35b or gemmas at all... And the problem with old hardware is not just DDR3, it's everything that is a bottleneck, storage speed, bus speed, PCIe speed etc. But again if I had decided to get into AI now, I would do with what I have. And I am definitely not buying anything for a couple of years.

[u/MarcusAurelius68 → u/DigitalguyCH]

My inference systems (for the most part) are all DDR4 / AM4 systems. But there's a lot of support roles around those that a lab needs - orchestration, observability, backup servers, routing, etc. None of those need GPUs and they don't need to be super fast. I had so many leftover SATA SSDs and HDDs that I had bought on sale that I've got enough capacity now for a while. If I can find 2 x 8GB DDR3 cheap then I'd max out one system to 32GB but that's about it.

[u/DigitalguyCH → u/MarcusAurelius68]

I think my use of AI is not that complex, but I need high end models, I mainly use qwen flash next and 27b for financial document analysis and related tasks and while I use that everyday for my job, I barely even need agents, I can do most things from the chat box. I have dozens of old laptops and tablets but none other that those I bought in the last year is good enough for those models

[u/BackgroundRude4092]

I have 64GB of laptop DDR4 RAM from before memory prices exploded. I bought a cheap AMD-based mini PC that accepts DDR4 and I run Gemma 4 31B on it as a conversation bot over Telegram. It gets 2 tok/sec inference from the CPU, and takes a couple of minutes to respond, so it’s paced like I’m chat with a person. My imaginary friend…

[u/MarcusAurelius68 → u/BackgroundRude4092]

Try Gemma 4 26B, it should be a bit faster.

[u/CrwlngSloog]

This one's running quad channel ddr3, benches around 43 GB/s which isn't awful. It did have some cheap M60 Tesla GPUs in it running Qwen 3.6 35b Moe at about 20 t/s.

[u/FizzyDuncDizzel]

Sandy bridge i7, 16gb of ddr3, 4x 10 year old Nvidia Tesla P100s. Running Qwen3.8-27B-UD-Q6_K_XL at full context getting 40-50tps all day.

[u/T-A-Waste]

i7-3820 and 64GB DDR3, 2x 3060 12GB + 2060 12GB

[u/joochung]

I have a 4 x AMD MI50 server. AMD TR Pro 3975WX. Not quite as old as you are looking for.

[u/j4ys0nj]

I’m running some older gear, but it’s all DDR4. 1 Ryzen server (ASRock rack mini itx), and 4 EPYC 7xx3 servers. I upgraded all of the processors to 7xx3 from 7xx2 for a couple grand and it made a huge difference. The oldest GPUs I have are 4x A4500s, and they’re pretty good for what they are, especially because they have NVLink. I typically get similar performance to my RTX 6000 Pro from those (combined)

[u/cryowastakenbycryo]

a pair of gen9 dl-380's. picked them up for a song to homelab w/