Flux – Compile an LLM to your hardware, then serve it (github.com)

Flux has introduced a groundbreaking execution planner and runtime designed specifically for optimizing the inference of large language models (LLMs) on hardware. By analyzing the performance of GPUs, CPUs, and storage, Flux efficiently organizes model layers and mixture-of-experts weights to maximize runtime speed. This meticulous evaluation process results in a constructed plan that can adapt to hardware limits—streaming additional model weights from NVMe storage when necessary—ensuring that memory constraints do not hinder performance. Flux offers compatibility with models on a pinned version of llama.cpp and can serve inference requests using an OpenAI-compatible API, making it highly versatile. The significance of Flux in the AI/ML community lies in its potential to enhance the deployment of LLMs by improving efficiency and responsiveness across various hardware configurations. With a unique focus on planning as model compilation, it provides an automated way to tailor models to specific systems, allowing developers to utilize their existing resources effectively. This capability is particularly important as models continue to grow in size and complexity. Key technical features include real-time performance monitoring, the ability to adjust for hardware changes, and support for multiple architectures (148 in total), setting a new standard for optimized model inference on consumer-grade hardware.