{"slug": "clarifying-my-original-idea-i-think-my-intention-was-misunderstood", "title": "Clarifying my original idea: I think my intention was misunderstood", "summary": "A forum poster clarified that their original proposal was not to use an NVMe SSD as VRAM, but to design future consumer GPUs with an expandable, lower-cost memory tier managed directly by the GPU, similar in concept to HBM. The poster argued that current offloading paths — GPU VRAM → PCIe → CPU/system memory → storage — add unnecessary data movement and latency, and that many consumer GPUs with 8GB, 12GB, or 16GB of VRAM are limited by memory capacity rather than compute. The proposal keeps fast VRAM for active workloads while adding a lower-cost tier closer to the GPU so larger AI models can run with a reasonable performance tradeoff.", "body_md": "I think my original post may have been misunderstood, so I would like to\n\nclarify what I actually meant.\n\nI was not suggesting that an NVMe SSD should simply be used as VRAM, or\n\nthat a GPU should access storage through the normal PCIe → CPU → system\n\nRAM path.\n\nMy idea was closer to the concept behind HBM: bringing additional memory\n\ncloser to the GPU and designing future consumer GPUs with expandable\n\nmemory in mind.\n\nThe reason I mentioned cheaper alternatives was because HBM is currently\n\ntoo expensive for consumer graphics cards. A practical consumer solution\n\nwould probably not be identical to HBM, but a new type of GPU memory\n\nexpansion layer designed specifically for AI workloads.\n\nThe important point is reducing the distance and overhead between the\n\nGPU and additional memory.\n\nA current offloading approach may look like:\n\nGPU VRAM → PCIe → CPU/system memory → storage\n\nThis introduces unnecessary data movement and latency.\n\nWhat I am suggesting is more like:\n\nGPU → directly managed additional memory tier\n\nwithout requiring data to travel through the CPU and general-purpose PC\n\nstorage path first.\n\nI understand that this would not provide the same bandwidth as HBM or\n\nGDDR VRAM. The goal is not to replace high-speed VRAM, but to create a\n\nmore practical memory hierarchy for AI workloads where models are\n\nincreasingly limited by memory capacity rather than compute power.\n\nFor example, many consumer GPUs today have enough compute capability but\n\nare restricted by 8GB, 12GB, or 16GB of VRAM. A better-designed memory\n\narchitecture could allow these GPUs to handle larger models with a\n\nreasonable performance tradeoff.\n\nSo my suggestion is not “use SSD as VRAM.”\n\nIt is: - keep fast VRAM for active workloads, - add a lower-cost\n\nexpandable memory tier closer to the GPU, - allow the GPU/runtime to\n\nmanage data movement efficiently.\n\nThe main idea is to rethink consumer GPU memory design for the AI era.", "url": "https://wpnews.pro/news/clarifying-my-original-idea-i-think-my-intention-was-misunderstood", "canonical_source": "https://discuss.huggingface.co/t/clarifying-my-original-idea-i-think-my-intention-was-misunderstood/180310#post_1", "published_at": "2026-09-12 05:44:14+00:00", "updated_at": "2026-09-12 05:56:38.330705+00:00", "lang": "en", "topics": ["ai-infrastructure", "ai-chips"], "entities": ["NVMe SSD", "HBM", "GDDR", "PCIe", "GPU", "VRAM"], "alternates": {"html": "https://wpnews.pro/news/clarifying-my-original-idea-i-think-my-intention-was-misunderstood", "markdown": "https://wpnews.pro/news/clarifying-my-original-idea-i-think-my-intention-was-misunderstood.md", "text": "https://wpnews.pro/news/clarifying-my-original-idea-i-think-my-intention-was-misunderstood.txt", "jsonld": "https://wpnews.pro/news/clarifying-my-original-idea-i-think-my-intention-was-misunderstood.jsonld"}}