Testing legacy limits: Forcing a local AI pipeline to successfully execute an OpenCL fallback on a 2013 Ivy-Bridge Laptop (Geforce 720M)

Hey r/selfhosted, I wanted to see what happens when you push local inference architectures onto absolute vintage consumer hardware. I dug out an old ASUS X75VC laptop (originally released back in December 2013) to run a strict CPU/legacy GPU fallback survival test. The Legacy Specs: Processors: 4 × Intel® Core™ i5-3200M CPU @ 2.60GHz (IVY-BRIDGE) Memory: 12 GiB of RAM (11.6 GiB usable) Graphics 1: Mesa Intel® HD Graphics 4000 Graphics 2: NVD7 Geforce GTS 720M Environment Configuration: NODEAI_VULKAN_SUPPORTED="FALSE" NODEAI_RUN_ACTIVE_BACKENDS_LIST="OPENCL" The Results: 🔴 CPU Fallback Render Time: 5 hours, 8 minutes, and 35 seconds. The Takeaway: The render time is obviously terrible for a production workflow, but the fallback engine layout actually works cleanly. Even on a 2013 machine with zero native Vulkan support, the backend pipeline successfully rerouted execution paths, handled the driver constraints, and completed the generation safely without throwing an out-of-memory kernel panic or crashing the underlying loop container. (Attached gallery showing the ComfyUI nodes tracking the multi-hour terminal clock execution loop, alongside the final output render). What is the oldest or weakest "potato" hardware you guys have successfully forced to finish an offline inference run without the kernel dropping out?