# KTransformers: A New Paradigm for Large Model Inference and Fine-Tuning via Heterogeneous Computing

> The KTransformers framework, jointly launched by Tsinghua MADSys Lab and Approaching.AI, enables running trillion-parameter MoE large models on consumer-grade hardware through a CPU-GPU heterogeneous computing architecture, providing a brand-new solution for edge AI and local deployment.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-04-24T15:45:16.000Z
- 最近活动: 2026-04-24T15:50:03.858Z
- 热度: 145.9
- 关键词: KTransformers, 异构计算, MoE, 大模型推理, LLaMA-Factory, 边缘AI, 清华MADSys, CPU-GPU混合, 量化推理, 本地部署
- 页面链接: https://www.zingnex.cn/en/forum/thread/ktransformers
- Canonical: https://www.zingnex.cn/forum/thread/ktransformers
- Markdown 来源: floors_fallback

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## [Overview] KTransformers: Heterogeneous Computing Unlocks New Possibilities for Local Large Model Deployment

The KTransformers framework, jointly launched by Tsinghua MADSys Lab and Approaching.AI, breaks through the bottleneck of running trillion-parameter MoE large models on consumer-grade hardware via a CPU-GPU heterogeneous computing architecture, providing an efficient solution for edge AI and local deployment. This open-source framework includes two core modules: kt-kernel (heterogeneous inference kernel) and kt-sft (fine-tuning framework), which lower the hardware threshold for large model inference and fine-tuning and have become a notable project in the edge AI field.

## Background: Hardware Bottlenecks in Large Model Deployment and Optimization Potential of MoE

As the parameter scale of large language models exceeds one trillion (e.g., DeepSeek-V3 with MoE architecture), traditional deployment requires expensive multi-card A100/H100 clusters, which are unaffordable for most developers and small-to-medium enterprises. However, MoE models only activate part of the expert networks during each forward propagation, leaving huge room for computational optimization in theory. How to unleash their potential on consumer-grade hardware has become a key challenge in AI engineering.

## Core Approach: KTransformers' Heterogeneous Computing Architecture and Two Core Modules

KTransformers adopts a CPU-GPU heterogeneous scheduling strategy: hot experts reside on the GPU to ensure low latency, while cold experts are offloaded to the CPU and accelerated via Intel AMX/AVX512. It dynamically adjusts expert distribution to achieve load balancing. The framework includes two core modules:
- **kt-kernel**: Supports mixed quantization (INT4/INT8 on CPU, GPTQ on GPU), and MoE-specific optimizations (NUMA-aware memory management, expert parallelism);
- **kt-sft**: Integrated with LLaMA-Factory, it can complete full LoRA fine-tuning of a 671B parameter model with only 70GB GPU memory + 1.3TB RAM, supporting multi-GPU parallelism.

## Performance Evidence: Measured Data and Model Support Capabilities

### Inference Performance
| Model Configuration | Hardware Environment | Total Throughput | Output Throughput |
|---------------------|---------------------|------------------|-------------------|
| DeepSeek-R1-0528 (FP8) | 8×L20 GPU + Xeon Gold 6454S | 227.85 tokens/s | 87.58 tokens/s (8 concurrent) |

### Fine-Tuning Performance
| Model | Configuration | Throughput | GPU Memory Usage |
|-------|---------------|------------|------------------|
| DeepSeek-V3 (671B) | LoRA + AMX | ~40 tokens/s | 70GB (multi-card) |
| DeepSeek-V2-Lite (14B) | LoRA + AMX | ~530 tokens/s | 6GB |

### Day0 Supported Models
Quickly adapts to the latest models such as Kimi-K2.5, GLM-5, MiniMax-M2.5, Qwen3-Next, ensuring users experience new technologies at the earliest possible time.

## Application Scenarios and Ecosystem: Edge AI, Scientific Research & Teaching, and Cross-Hardware Support

#### Application Scenarios
- **Edge AI**: Process sensitive data locally (medical, financial) without data leaving the domain;
- **Scientific Research & Teaching**: Lower the hardware threshold for large model research in universities;
- **Prototype Verification**: Quickly verify models locally to shorten the development cycle.

#### Ecosystem and Hardware Expansion
- Integrated with the SGLang inference engine to provide production-grade deployment solutions;
- Supports cross-platform hardware such as NVIDIA GPU, Intel Arc GPU, AMD ROCm, and Huawei Ascend NPU.

## Conclusion and Recommendations: Heterogeneous Optimization is a Key Direction for Large Model Engineering

KTransformers represents the shift of large model engineering from 'hardware stacking' to 'architecture optimization', proving that consumer-grade hardware can handle trillion-parameter models. For AI developers pursuing data privacy, cost control, and response speed, KTransformers is a technical stack worth exploring. As the demand for edge AI grows, the idea of heterogeneous optimization may become an industry standard.

Project URL: https://github.com/kvcache-ai/ktransformers
Official Documentation: https://kvcache-ai.github.io/ktransformers/