# ktransformers | kvcache.ai | Knowledge Base | Instagit

A Flexible Framework for Experiencing Heterogeneous LLM Inference/Fine-tune Optimizations

GitHub Stars: 18.7k

Repository: https://github.com/kvcache-ai/ktransformers

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## Articles

### [How to Troubleshoot Common KTransformers Inference Issues: VRAM and Weight Loading](/kvcache-ai/ktransformers/troubleshoot-kt-inference-issues)

Troubleshoot KTransformers inference issues like VRAM exhaustion and weight loading problems. Learn to fix GPU expert masks, file handles, and memory optimizations for smoother operations.

- Tags: how-to-guide
- Published: 2026-07-26

### [How to Fine-Tune Ultra-Large MoE Models Like DeepSeek-V3 on Limited GPU Memory with KTransformers](/kvcache-ai/ktransformers/fine-tune-large-moe-models-limited-gpu-memory-kt)

Fine-tune large MoE models like DeepSeek-V3 on limited GPU memory with KTransformers. Learn how to dynamically allocate expert tensors and stream weights from CPU for efficient training.

- Tags: how-to-guide
- Published: 2026-07-26

### [How KTransformers Enables ROCm Support for AMD GPUs: A Complete Build Guide](/kvcache-ai/ktransformers/kt-handle-rocm-amd-gpus)

Learn how KTransformers brings ROCm support to AMD GPUs. This guide details the build process, enabling GPU acceleration for your AI models.

- Tags: how-to-guide
- Published: 2026-07-26

### [How to Set Up KTransformers with Ascend NPU for Inference: A Complete Guide](/kvcache-ai/ktransformers/setup-kt-ascend-npu-inference)

Learn how to set up KTransformers with Ascend NPU for efficient MoE inference. Follow this guide for high-performance results using CANN and Docker.

- Tags: how-to-guide
- Published: 2026-07-26

### [How to Enable AVX2-Only CPU Backend for KTransformers Inference](/kvcache-ai/ktransformers/enable-avx2-only-cpu-backend-kt-inference)

Enable KTransformers AVX2-only CPU backend for faster inference. Learn how to set the CPUINFER_CPU_INSTRUCT environment variable for Intel Haswell+ and AMD Zen+ processors.

- Tags: how-to-guide
- Published: 2026-07-26

### [How to Convert and Optimize Model Weights for KTransformers Inference Using convert_cpu_weights.py](/kvcache-ai/ktransformers/convert-optimize-model-weights-kt-inference)

Learn how to convert and optimize model weights for KTransformers CPU inference using convert_cpu_weights.py. Quantize weights to INT4 or INT8 for faster performance.

- Tags: how-to-guide
- Published: 2026-07-26

### [KTransformers Linear Operations Backends: Llamafile and Tinyblas Explained](/kvcache-ai/ktransformers/kt-supported-backends-linear-operations)

Explore KTransformers linear operations backends: Llamafile and TinyBLAS. Discover optimized CPU kernels for ARM and AMD x86 64 architectures.

- Tags: deep-dive
- Published: 2026-07-26

### [How KTransformers Compares to vLLM and Other Inference Frameworks: CPU-First MoE vs GPU-Centric Design](/kvcache-ai/ktransformers/kt-vs-vllm-inference-framework-comparison)

Compare KTransformers CPU-first MoE inference with vLLM's GPU-centric design. Discover KTransformers' specialized MoE scheduling and high-performance C++ kernels for efficient AI model execution.

- Tags: comparison
- Published: 2026-07-26

### [How to Set Up KTransformers on Windows Native Environment: Complete Installation Guide](/kvcache-ai/ktransformers/setup-kt-windows-native-environment)

Install KTransformers on Windows natively. Follow our guide to set up CUDA, Python, and MSVC, then deploy the pre-compiled wheel or run install bat for a fast LLM inference engine without WSL2.

- Tags: how-to-guide
- Published: 2026-07-26

### [How to Implement Long Context Inference (Up to 139K Tokens) with KTransformers](/kvcache-ai/ktransformers/implement-long-context-inference-kt)

Unlock long context inference up to 139K tokens with KTransformers. Learn how to split and offload KV-cache for efficient processing, keeping active data on GPU.

- Tags: how-to-guide
- Published: 2026-07-26

### [How to Enable FP8 Per-Channel Precision and Native BF16 Inference in KTransformers](/kvcache-ai/ktransformers/enable-fp8-bf16-inference-kt)

Learn to boost KTransformers performance with FP8 per-channel precision and native BF16 inference. Optimize your models today!

- Tags: how-to-guide
- Published: 2026-07-26

### [KTransformers Supported Models: DeepSeek, Qwen, GLM-4, MiniMax & Kimi-K2](/kvcache-ai/ktransformers/kt-supported-llm-models)

Discover KTransformers supported models including DeepSeek, Qwen, GLM-4, MiniMax, and Kimi-K2. Use our centralized registry for fast, easy inference.

- Tags: api-reference
- Published: 2026-07-26

### [How to Configure Multi-GPU Inference with Heterogeneous Expert Placement in KTransformers](/kvcache-ai/ktransformers/configure-multi-gpu-inference-heterogeneous-expert-placement-kt)

Learn how to configure multi-GPU inference with heterogeneous expert placement in KTransformers. Run massive MoE models across GPUs with limited VRAM by intelligently placing experts.

- Tags: how-to-guide
- Published: 2026-07-26

### [How to Perform DPO Fine-Tuning with KTransformers: A Complete LLaMA-Factory Guide](/kvcache-ai/ktransformers/perform-dpo-fine-tuning-kt)

Learn DPO fine-tuning with KTransformers and LLaMA Factory. Optimize massive MoE models on consumer GPUs by leveraging CPU CPU/AMX accelerators. Get started today.

- Tags: how-to-guide
- Published: 2026-07-26

### [KTransformers SFT vs ZeRO-Offload Speedup: How Hybrid CPU-GPU Placement Delivers 6-12× Faster MoE Training](/kvcache-ai/ktransformers/kt-sft-vs-zero-offload-speedup-comparison)

Discover how KTransformers SFT achieves 6-12x faster MoE training than ZeRO-Offload by optimizing CPU-GPU expert placement reducing memory usage and eliminating per-step data transfers.

- Tags: performance
- Published: 2026-07-26

### [How to Set Up NUMA-Aware Memory Management for KTransformers Performance](/kvcache-ai/ktransformers/setup-numa-aware-memory-management-kt-performance)

Boost KTransformers performance with NUMA-aware memory management. Learn to configure topology, thread pools, and model weights for optimal speed.

- Tags: performance
- Published: 2026-07-26

### [Understanding KTransformers' 3-Layer GPU-CPU-Disk Prefix Cache Architecture](/kvcache-ai/ktransformers/kt-3-layer-gpu-cpu-disk-prefix-cache-architecture)

Explore KTransformers' 3-layer GPU-CPU-Disk prefix cache architecture. Accelerate LLM inference with sub-millisecond hot prefix access and efficient cold data management.

- Tags: architecture
- Published: 2026-07-26

### [How to Integrate KTransformers with SGLang for Heterogeneous LLM Serving](/kvcache-ai/ktransformers/integrate-kt-sglang-llm-serving)

Learn to integrate KTransformers with SGLang for efficient LLM serving. Optimize performance by routing experts to CPU and GPU and leverage NUMA-aware thread pools.

- Tags: how-to-guide
- Published: 2026-07-26

### [KTransformers Quantization Formats Supported: INT4, INT8, FP8, GPTQ, and IQ1_S Explained](/kvcache-ai/ktransformers/kt-supported-quantization-formats)

Explore KTransformers quantization formats like INT4, INT8, FP8, GPTQ, and IQ1_S. Discover automatic hardware backend selection for optimal performance.

- Tags: deep-dive
- Published: 2026-07-26

### [How KTransformers Handles MoE Expert Scheduling Between CPU and GPU](/kvcache-ai/ktransformers/kt-handle-moe-expert-scheduling-cpu-gpu)

Discover how KTransformers optimizes MoE expert scheduling between CPU and GPU using an activation frequency scheduler. Run large models with limited VRAM by placing frequent experts on GPU.

- Tags: internals
- Published: 2026-07-26

### [How to Configure Intel AMX Acceleration (AMX-INT8/AMX-BF16) in KTransformers](/kvcache-ai/ktransformers/configure-intel-amx-acceleration-kt)

Learn how to configure Intel AMX acceleration like AMX-INT8 and AMX-BF16 in KTransformers. Unlock faster AI model performance with this quick guide.

- Tags: how-to-guide
- Published: 2026-07-26

### [KT-Kernel CPU-GPU Heterogeneous Computing Architecture Explained](/kvcache-ai/ktransformers/kt-kernel-architecture-cpu-gpu-heterogeneous-computing)

Explore the KT-Kernel architecture for CPU-GPU heterogeneous computing. Learn how it optimizes inference by running MoE layers across processors for peak performance. Discover the ktransformers innovation at kvcache-ai.

- Tags: architecture
- Published: 2026-07-26

### [How KTransformers Achieves 3-28× Speedup for DeepSeek-R1/V3 Inference on Consumer Hardware](/kvcache-ai/ktransformers/how-kt-achieve-3-28x-speedup-deepseek-inference)

Unlock 3-28x faster DeepSeek-R1/V3 inference on consumer hardware with KTransformers using FP8 quantization, zero-copy loading, fused CUDA kernels, and AMX INT8 computation. Eliminate bottlenecks.

- Tags: performance
- Published: 2026-07-26

### [KTransformers Example Scripts: A Complete Guide to Kernel Testing and Fine-Tuning](/kvcache-ai/ktransformers/are-there-example-scripts-for-ktransformers)

Explore KTransformers example scripts for kernel testing, Python API integration, and fine-tuning workflows. Discover efficient ways to test and optimize your models.

- Tags: how-to-guide
- Published: 2026-07-21

### [Deploying KTransformers in Production Environments: A Complete Hardware and Software Guide](/kvcache-ai/ktransformers/ktransformers-best-practices-production-deployment)

Deploy KTransformers in production environments with this hardware and software guide. Learn best practices for version matching, containerization, and reproducible inference.

- Tags: best-practices
- Published: 2026-07-20

### [KTransformers Model Architectures: Supported Backends and Custom Integration Guide](/kvcache-ai/ktransformers/ktransformers-supported-model-architectures-add-new-models)

Explore KTransformers model architectures like Qwen2MoE, DeepSeek V2/V3, LLaMA, and GLM-4 MoE. Learn to add custom models by implementing wrappers and GGUF weight loading.

- Tags: deep-dive
- Published: 2026-07-20

### [How KTransformers Handles 8K+ Long Contexts on 24GB VRAM: MLA and FlashInfer Explained](/kvcache-ai/ktransformers/ktransformers-long-context-handling-limited-vram)

Discover how KTransformers efficiently processes 8K+ token contexts on 24GB VRAM using MLA kernels and FlashInfer. Learn to compress KV-caches and fit models within memory limits.

- Tags: deep-dive
- Published: 2026-07-20

### [Debug kt-kernel Installation Issues Related to CPU Variant Detection](/kvcache-ai/ktransformers/kt-kernel-debug-installation-cpu-variant-detection)

Debug kt-kernel installation errors caused by CPU variant detection mismatches. Learn how to resolve link errors and performance issues for optimal compilation.

- Tags: how-to-guide
- Published: 2026-07-20

### [AMX, AVX512, and AVX2 Backends in kt-kernel: Key Differences Explained](/kvcache-ai/ktransformers/kt-kernel-backends-amx-avx512-avx2-comparison)

Discover the key differences between AMX, AVX512, and AVX2 backends in kt-kernel. Understand how each targets different CPU instruction sets for optimized performance in your AI applications.

- Tags: deep-dive
- Published: 2026-07-20

### [How to Integrate KTransformers with SGLang for Production Model Serving](/kvcache-ai/ktransformers/ktransformers-integrate-sglang-production-serving)

Integrate KTransformers with SGLang for efficient production model serving. Learn to enable heterogeneous inference with GPU and CPU offloading for optimized performance.

- Tags: how-to-guide
- Published: 2026-07-20