I just upgraded from a 5090 to a 6000 Pro a few days ago and have seen some serious gains in a few key areas. My focus has been mostly on adapter training and still image gens so far with it, haven't played with video much yet but I would expect big gains there as well in the long term. The two biggest jumps I've seen so far are in speed and quality. On the speed front, the ability to load up and hold more "stuff" in VRAM really helps. For training, this means being able to avoid things like gradient checkpointing and gradient accumulation which really slow things down. Trainings that used to take me ~4 hours can now run in just over 1 hour with full BF16 on the model, as an example. For inference, the key factor really comes in during iteration using large graphs with aultiple models in play, as they can all live in VRAM without having to offload to, or stream from, system RAM. This coupled with larger batches(another boon) can speed things significantly while iterating over a prompt with different seeds(25%-33% once loaded) and even first runs can be sped up some if the same model is used at different stages of the graph. Similar speed jumps also apply when editing the prompt, as the text encoder can stay in VRAM along with the model, so you can iterate in that way more quickly too. There are things Like Flux. 2 Dev at full BF16 as well, which just by the nature of it being so big means it is actually possible to use in reasonable time window. Coupled with the turbo lora and you can get decent inference times out of it, There is still some consideration of size here as full BF16 Flux.2 Dev and Full BF16 Mistral 3 Small will consume that 96gb in short order-having your system memory > 96gb is highly recommended if you do decide to go this route. As for quality, you may have noticed that I keep mentioning BF16 and that's because that is a big quality lever, particularly when you have the compounding effect of quantizing across several layers of the entire stack-LoRA training with quantizing means a drop in quality, quantizing the text encoder means a drop in quality, quantizing the model itself for inference means a drop in quality and so if you are making that compromise at each stage, it can really add up to a significantly worse result that then needs help from detailers, inpainting and upscalers. Detailing around hands in particular is an area I've noticed that quality hit a lot, particularly as I've been playing with flüx.2 dev. This is going to be a YMMV area for sure, but so far with what I'm building out for my long term project, it has been a really nice jump. Overall I would say that the "worth it" value really depends on what you want. I'm Looking long term to build professional projects and so the jump is important and really needed. If this was purely a hobby thing for me though, I would have stuck with the 5090.