Ktransformers is a comprehensive framework designed for the operation, fine-tuning, and serving of large language models. It functions as a heterogeneous inference engine and quantized execution runtime, enabling the deployment of massive models by distributing computational workloads across both CPU and GPU resources. This architecture allows users to bypass local memory constraints, making it possible to run and train models that exceed the capacity of a single device.
The project distinguishes itself through specialized support for sparse architectures, particularly mixture-of-experts models. It employs pipelined expert offloading and layer-wise sharding to balance memory usage and processing speed across heterogeneous hardware. By utilizing hardware-specific kernel optimizations, such as specialized instruction sets for server processors, the framework maximizes throughput for both inference and fine-tuning tasks.
Beyond its core execution capabilities, the project provides a production-ready serving environment that exposes models via an OpenAI-compatible HTTP interface. It includes a suite of command-line tools for managing model deployments, configuring system environments, and performing performance benchmarking. The framework also supports the integration of custom inference kernels and operator injection, allowing for architectural modifications and fine-tuned control over model placement strategies.
