Introduction
KTransformers is an open-source framework focused on optimizing LLM inference through kernel injection, prefix caching and multi-level acceleration strategies. It aims to speed up generation and reduce memory usage across desktop and cluster deployments.
Key features
- Kernel injection to replace native modules with optimized kernels.
- Multi-level prefix cache (GPU-CPU-Disk) to improve throughput for long contexts.
- Compatibility with Transformers API and multiple model formats (GGUF, safetensors).
- Extensive documentation, tutorials and demos for deployment and injection.
Use cases
- Desktop inference: run large models efficiently on limited VRAM machines.
- Server-side deployment: accelerate inference on multi-GPU clusters.
- Research: prototype and benchmark new kernels, quantization and MoE strategies.
Technical characteristics
- Python-first user API with C++/CUDA performance kernels under the hood.
- Support for ROCm, AMX, FP8 and other hardware features.
- Active development with frequent updates for new models and kernels.