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KTransformers explores LLMs with limited VRAM
Published on July 17, 2026, the report describes an open framework for heterogeneous inference and fine-tuning of language models.
On July 17, 2026, a report about KTransformers described an open-source framework aimed at optimizing heterogeneous inference and fine-tuning of language models. According to the text, it can run large models with a 139K-token context on a GPU with 24 GB of VRAM, keeping some experts on the CPU.
Distributing experts across CPU and GPU is presented as a way to explore inference when video memory is limited; the report does not detail measurements or test conditions. To verify the result, consult the original publication and check its technical description and reported tests. If you use AI to study or apply these ideas, protect internal data under your organization’s policy and avoid submitting confidential information.
Original source: KTransformers busca otimizar inferência e fine-tuning heterogêneos de LLMs