Nvidia RTX 5060

Nvidia's RTX 5060 is usable for current PyTorch and image-generation stacks, but older packaged environments such as Fooocus's official portable still default to torch==2.1.0 and xformers==0.0.23, causing the bundled PyTorch to reject the card's sm_120 compute capability and fail with "no kernel image is available for execution on the device." PyTorch added Blackwell support with CUDA 12.8 builds in PyTorch 2.7, and the article recommends establishing a clean baseline with ComfyUI's current Windows portable, whose maintained stack manifest includes CUDA builds covering compute capability 12.0, or Forge Neo, which targets current PyTorch/CUDA versions and can fall back to native PyTorch scaled-dot-product attention. The 8 GB of VRAM on the RTX 5060 remains a separate constraint from the packaging issue.

Yeah. Quite a few projects still ship official builds with an older CUDA/PyTorch stack aimed at pre-Blackwell GPUs. In many cases, updating PyTorch may be enough to make the core app usable. If I were choosing a GUI for Blackwell today, I’d probably use Forge Neo or ComfyUI… I would not generalize this to “the RTX 5060 is incompatible with image-generation software” as a whole. There is a real compatibility problem with some older packaged environments. Fooocus is a good example: its current launcher still defaults to torch==2.1.0 and xformers==0.0.23 , while RTX 5060 users have reported the bundled PyTorch rejecting the card’s sm 120 compute capability and then failing with no kernel image is available for execution on the device . But that is different from the GPU itself being unusable. PyTorch added Blackwell support with CUDA 12.8 builds in PyTorch 2.7, and current image-generation environments can use much newer CUDA/PyTorch stacks. So for a new RTX 5060 installation, my default route would be: That separates “does Blackwell work at all?” from “does this particular extension/kernel work on Blackwell?” ComfyUI is especially straightforward as a baseline at the moment: its current Windows portable uses a modern PyTorch/CUDA stack, and its maintained stack manifest explicitly includes CUDA builds covering compute capability 12.0. Forge Neo is also actively targeting current PyTorch/CUDA versions. One useful design choice there is that it can fall back to native PyTorch scaled-dot-product attention rather than requiring every optional acceleration library. Its own README specifically warns against blindly installing every attention backend. Why this can be confusing on RTX 50-series So I think the practical picture is closer to: RTX 5060 itself: usable for current PyTorch/image-generation stacks old packaged PyTorch: can definitely be a problem current ComfyUI / Forge Neo: good places to establish a clean Blackwell baseline Fooocus official portable: old bundled runtime is a known problem RuinedFooocus: newer runtime management; should be evaluated separately xFormers / Flash / Sage / custom nodes: test separately from core GPU support 8 GB VRAM: still a real constraint, but a different problem For someone who just wants to generate images rather than debug Python packaging, I would probably start with a clean current ComfyUI or Forge Neo installation, verify one ordinary generation, and only then recreate the desired Fooocus/extensions workflow. That should tell you very quickly whether you are dealing with the RTX 5060 itself, an old packaged runtime, or one particular optional component.