For at-home hobbyist setups VRAM is almost always your main bottleneck, followed by RAM. It’s not even speed so much as capacity. If you really want to budget a system, get a 16gb 5060 first, which is about as good a bang for your buck as you can get for starting with AI at home. Then build around that. DDR clock speed can also be helpful because anything that doesn’t fit into VRAM has to go there. 16GB of VRAM can fit the new Gemma4 12B QAT model easily, which sits at 6.5GB at quant4 giving you tons of room for context. Gemma4 26B which is an MOE model can also fully fit with less room for context (or more if you drop some tensors). Both will give you great performance. I’d recommend 32GB of RAM for that setup unless you plan on shoving a second 5060 in there later for bigger models, then maybe go for 64gb of RAM. It all depends on how much money you are working with. Nvidia still is king as ROCM and Vulcan still aren’t up to par with CUDA for AI applications, unfortunately. AMD or Intel doesn’t matter for your CPU.
Additionally, if you are playing with 16gb of RAM you might as well grab StabilityMatrix and download ComfyUI through it, then link up a CivitAI account and start downloading checkpoints and LORAs. Then you can play with video generation (LTX 2.3) and image generation (Illustrious, Anima, ZimageTurbo, Flux Klein 9B, Ideogram 4.0, whatever take your pick all are good for their own things). With 16GB you can even create your own LORAs with AItoolkit. I’m able to create LORAs with 8gb of VRAM but it is very slow and has some limitations.
For at-home hobbyist setups VRAM is almost always your main bottleneck, followed by RAM. It’s not even speed so much as capacity. If you really want to budget a system, get a 16gb 5060 first, which is about as good a bang for your buck as you can get for starting with AI at home. Then build around that. DDR clock speed can also be helpful because anything that doesn’t fit into VRAM has to go there. 16GB of VRAM can fit the new Gemma4 12B QAT model easily, which sits at 6.5GB at quant4 giving you tons of room for context. Gemma4 26B which is an MOE model can also fully fit with less room for context (or more if you drop some tensors). Both will give you great performance. I’d recommend 32GB of RAM for that setup unless you plan on shoving a second 5060 in there later for bigger models, then maybe go for 64gb of RAM. It all depends on how much money you are working with. Nvidia still is king as ROCM and Vulcan still aren’t up to par with CUDA for AI applications, unfortunately. AMD or Intel doesn’t matter for your CPU.
Additionally, if you are playing with 16gb of RAM you might as well grab StabilityMatrix and download ComfyUI through it, then link up a CivitAI account and start downloading checkpoints and LORAs. Then you can play with video generation (LTX 2.3) and image generation (Illustrious, Anima, ZimageTurbo, Flux Klein 9B, Ideogram 4.0, whatever take your pick all are good for their own things). With 16GB you can even create your own LORAs with AItoolkit. I’m able to create LORAs with 8gb of VRAM but it is very slow and has some limitations.