LLM inference with limited VRAM
A post dated July 17, 2026 describes a framework for heterogeneous inference and fine-tuning, distributing experts across GPU and CPU.
Source: KTransformers busca otimizar inferência e fine-tuning heterogêneos de LLMs (github.com). Text prepared with AI from this source.
What happened and what to do
The post, published on July 17, 2026, presents KTransformers, an open-source framework for optimizing heterogeneous inference and fine-tuning of language models. According to the text, the approach can run models with a 139K-token context using 24 GB of VRAM, while keeping some experts on the CPU.
For a company with limited GPU resources, this strategy can be assessed through a proof of concept using representative models, workloads, and hardware. Implementation can include performance tests, CPU and GPU usage monitoring, and operational criteria for deciding whether this architecture meets requirements.
How the consultancy can help
Wendelmaques can diagnose the opportunity and infrastructure constraints, define a proof of concept, and implement and operate a solution sized for the company's environment.
Next step
Send a short description of your case to receive a scoped proposal for diagnosis and implementation.
Let's assess your project
I reply with scope and a proposal.