What is ktransformers?
KTransformers is a Python-centric framework designed to optimize large language model (LLM) inference on resource-constrained hardware. By integrating kernel-level optimizations, strategic offloading, and a flexible injection system, it enables users to run state-of-the-art models like DeepSeek-Coder-V3 (671B parameters) on desktops equipped with as little as 24GB VRAM.
Why KTransformers Matters
Running large language models locally often demands expensive GPUs and extensive technical expertise. KTransformers addresses these challenges by:
Reducing hardware barriers: Execute massive models on consumer-grade hardware without compromising performance.
Enhancing speed: Achieve up to 28x faster prefill speeds and 3x faster decode speeds compared to traditional methods.
Simplifying deployment: Utilize YAML-based templates to inject optimized kernels and manage complex configurations effortlessly.
Whether you're a developer, researcher, or enterprise user, KTransformers empowers you to experiment with cutting-edge models while keeping costs and complexity low.
Key Features
✨ Efficient Kernel Optimizations
Leverage high-performance CPU and GPU kernels such as Marlin and Llamafile for quantized models, achieving up to 3.87x acceleration in matrix computations.
✨ Flexible Injection Framework
Replace original PyTorch modules with optimized variants using simple YAML templates. Combine multiple optimizations seamlessly to explore their synergistic effects.
✨ Heterogeneous Computing Support
Intelligently offload compute-intensive tasks between GPU and CPU, reducing VRAM usage while maintaining high throughput.
✨ RESTful API and Web UI Compatibility
Integrate KTransformers with OpenAI/Ollama APIs or deploy a ChatGPT-like web interface for local use.
✨ Upcoming Open Source Contributions
Features like AMX optimizations and selective expert activation will soon be open-sourced, fostering community-driven innovation.
Real-World Use Cases
1. Local Development with VSCode Copilot
Run a GPT-4-level code assistant on your desktop with just 24GB VRAM. Developers can integrate KTransformers into VSCode via its OpenAI-compatible API, enabling real-time code suggestions and completions without relying on cloud services.
2. Long-Sequence Text Processing
Process lengthy documents or analyze extensive codebases efficiently. With Intel AMX-powered CPU optimizations, KTransformers achieves 286 tokens/s prefill speed, reducing processing times from minutes to seconds.
3. Enterprise-Scale Local Deployment
Deploy large models like DeepSeek-Coder-V2 for internal applications such as customer support chatbots or content generation tools. By running these models locally, businesses save on cloud costs while ensuring data privacy.
Conclusion
KTransformers bridges the gap between powerful LLMs and accessible hardware. Its innovative optimizations, ease of use, and focus on extensibility make it ideal for developers, researchers, and enterprises alike. Whether you're building a personal AI assistant or deploying enterprise-grade solutions, KTransformers ensures you get the most out of your hardware.
Explore the project today at GitHub.
Frequently Asked Questions
Q: What hardware do I need to run KTransformers?
A: KTransformers supports local deployments on systems with as little as 24GB VRAM and sufficient DRAM (e.g., 136GB for DeepSeek-Coder-V2).
Q: Can I use KTransformers with non-MoE models?
A: Yes, KTransformers is compatible with various architectures, including MoE and dense models.
Q: Is KTransformers fully open source?
A: The core framework is available as a preview binary distribution. Upcoming features like AMX optimizations will be open-sourced in version 0.3.
Q: How does KTransformers compare to vLLM?
A: While vLLM focuses on large-scale deployments, KTransformers specializes in optimizing local inference for resource-constrained environments.
More information on ktransformers
Launched
Pricing Model
Free
Starting Price
Global Rank
Follow
Month Visit
<5k
Tech used
ktransformers was manually vetted by our editorial team and was first featured on 2025-02-18.
ktransformers Alternatives
Load more Alternatives-
Transformer Lab: An open - source platform for building, tuning, and running LLMs locally without coding. Download 100s of models, finetune across hardware, chat, evaluate, and more.
-
-
OLMo 2 32B: Open-source LLM rivals GPT-3.5! Free code, data & weights. Research, customize, & build smarter AI.
-
Kolosal AI is an open-source platform that enables users to run large language models (LLMs) locally on devices like laptops, desktops, and even Raspberry Pi, prioritizing speed, efficiency, privacy, and eco-friendliness.
-
A RWKV management and startup tool, full automation, only 8MB. And provides an interface compatible