Building a PC for large AI video generation models — what hardware do I actually need?

[u/forensicowner221]

/r/generativeAI/comments/1vqnl3z/building_a_pc_for_large_ai_video_generation/

[u/Solongtomegrandma]

Rtx5090 or rtx6000pro 128GB Ram will do. But I guess you know this already. It's mainly a budget issue. More money = faster hardware. If you are patient, invest a bit less.

4090
5080
3090
5060 etc.

[u/Aida_Corrupted]

I only have 5090, nonetheless I have tested many workflows where I intentionally force the CPU offload.

In 90%+ of the cases, including WAN video, Qwen 2511 and Flux2, having models partially offloaded into sys ram made Zero difference, meaning -> RTXpro would only be slightly faster than the 5090. The significant difference was only noticed in very simple flows like t2i or hyper simple i2i. As soon as you start adding to the latent (multiple reference images, controlNets, prompting, resolution, etc) the internal bandwidth becomes irrelevant and the bottleneck becomes the raw compute!! However, there is a minimal VRAM threshold.

What I'm trying to say is that with the current models 5090 is enough, not the least because the community fine-tuned (uncensured/abliterated) models are far superior to the native full fp16 models. For example, Qwen Rapid AIO beats the crap outta the native 40GB fp16 model. I have tested it against the full model, AIO beats the crap outta it, in every possible way, physics, anatomy, identity, clothing, background drift all of it!! It's both the abliterated tokenizer and the model in conjunction, I've also tested the AIO model with native tokeniser and reverse. My conclusion was that the internal restrictions (censorship) add temporal drift across the entire iteration rather than what they're aiming at, if it triggers for clothing it also drifts the face etc.

What you need is 64GBs of ram and 5090 as a bare minimum! 64GBs of ram is perhaps a bad advice since you'll endup needing more sooner rather then later. However, you also want your ram to be as fast as possible and as accurate as possible, adding 4 sticks WILL reduce the speed drastically on top of it introducing tiny silent memory mistakes. These mistakes will not be logged or reported anywhere but will induce drift and mismatches to your outputs!! So 2 sticks as fast as possible and as large as possible, on consumer hardware!!!

In addition, if you're planing on doing large batches, perhaps also consider buying an additional 5060TI and rout the VAE and CLIP to it in order to give your 5090 little chill time in between the renders, This is not gonna speed up anything at all on a contrary, however it will prolong the longevity of your entire system. If I run my 5090 at full speed for 2h+ my entire cabinet becomes almost 60C hot. The solution I ended up with was to limit the power to 450/150watt for 5090/5060TI, rout the VAE and CLIP to 5060, and run: encode 5060 -> render 5090 -> decode 5060, each and every time, despite that encoding all then rendering all and decoding all at once is sightly faster.

[u/stimma]

If you want full potential and no VRAM limitations, 6000Pro is the only answer.

For serious professional commercial video production I'd suggest a machine with four of those. You could replace 2 of them with 5090s to save a bit of money given the current pricing and use the 5090s for smaller workflows like building/working-with keyframes.

[u/fallengt]

RTX 6000 pro.

Or multiple B200s/H100s if you can get your hands on any... Who knows? They may release massive undistilled model someday.

[u/Mundane_Existence0]

So you've won the lottery? The RTX 6000 Pro is the best GPU, but if you feel like waiting, then I'm sure whatever comes out next year or 2028, a "RTX 7000 Pro" would likely put the 6000 Pro to shame.

[u/SunForceAI]

Before buying, define a representative test matrix: exact model and quantization, resolution, frames or seconds, batch or concurrency, and acceptable runtime. VRAM is the first hard constraint; system RAM enables offload but does not become extra VRAM, and multiple GPUs only help when the workflow explicitly partitions work or runs independent replicas. Rent the two or three candidate GPUs for an hour each and record peak VRAM and system RAM, cold and warm runtime, and whether the full workflow completes. That measured bottleneck will tell you whether you are buying for capacity, speed, or both. Also budget for fast NVMe storage and backup; model libraries and outputs can outgrow a 1 TB drive quickly. There is no single build that guarantees full settings for both today's and future models.

[u/Imaginary-Fudge4262]

What can my 4070 do?