{"slug": "ai-video-rendering-offloading-rtx-3090-workloads-to-128gb-ram-or-an-hp-zgx-nano", "title": "AI Video Rendering: Offloading RTX 3090 Workloads to 128GB RAM or an HP ZGX Nano?", "summary": "A practitioner recommends ComfyUI's comfy-aimdo allocator, an open-source tool from Comfy-Org that pages model weights from disk to the GPU on demand, as the way to run AI video rendering on a 24GB RTX 3090 instead of buying 128GB of RAM or an HP ZGX Nano. The allocator works with mmap'd weights so multi-GPU PyTorch inference processes share memory rather than duplicating copies in CPU RAM, and it overlaps weight loading with computation so transfer time stays small relative to compute time. The practitioner warns that 24GB of VRAM is only sufficient for smaller reference sizes or FL2VA and T2VA generations, since H3 Ref2VA runs with about 15 seconds of high-resolution reference video can reach 300k-400k tokens to denoise and exceed the VRAM budget.", "body_md": "I recommend using the same memory management system ComfyUI uses: [GitHub - Comfy-Org/comfy-aimdo: AI Model Demand Offloading Allocator · GitHub](https://github.com/Comfy-Org/comfy-aimdo).\n\nIt does on-demand “paging” of model weights from disk to the GPU. It works with mmap’d weights which is important with multi-GPU inferencing engines in PyTorch as your different per-rank processes share the memory instead of making a bunch of copies in CPU RAM.\n\nI am using this approach in my own image/video-gen software and it works well.\n\nIt does have a cost, you do have to wait for the weights to transfer over if they are being loaded in - but this ends up being a small amount of time compared to the compute time and it does attempt to overlap loading during computation.\n\nThat said, you do need to have enough VRAM to keep your activations and the current/next block of the model weights. With H3 Ref2VA generations, if you include ~15 seconds of reference video at a high resolution, you can easily reach 300k-400k tokens to denoise and that will exceed your VRAM budget. 24GB would be fine for smaller reference sizes (or doing FL2VA or T2VA generations).", "url": "https://wpnews.pro/news/ai-video-rendering-offloading-rtx-3090-workloads-to-128gb-ram-or-an-hp-zgx-nano", "canonical_source": "https://forum.level1techs.com/t/ai-video-rendering-offloading-rtx-3090-workloads-to-128gb-ram-or-an-hp-zgx-nano/258982#post_2", "published_at": "2026-10-19 21:53:17+00:00", "updated_at": "2026-10-19 22:17:31.021065+00:00", "lang": "en", "topics": ["ai-tools", "ai-infrastructure", "generative-ai", "ai-products"], "entities": ["ComfyUI", "Comfy-Org", "comfy-aimdo", "Nvidia RTX 3090", "HP ZGX Nano", "PyTorch"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/ai-video-rendering-offloading-rtx-3090-workloads-to-128gb-ram-or-an-hp-zgx-nano", "markdown": "https://wpnews.pro/news/ai-video-rendering-offloading-rtx-3090-workloads-to-128gb-ram-or-an-hp-zgx-nano.md", "text": "https://wpnews.pro/news/ai-video-rendering-offloading-rtx-3090-workloads-to-128gb-ram-or-an-hp-zgx-nano.txt", "jsonld": "https://wpnews.pro/news/ai-video-rendering-offloading-rtx-3090-workloads-to-128gb-ram-or-an-hp-zgx-nano.jsonld"}}