KTransformers Complete Guide: Run DeepSeek-R1 on 24GB VRAM with This CPU-GPU Collaboration Framework

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"I want to run a giant AI like DeepSeek-R1 on my own PC." "But GPU memory (VRAM) isn't enough, so I gave up…"

For you, there's a technology getting attention in 2026: KTransformers.

With it, you can run DeepSeek-R1-class giant models with just 24GB of VRAM. Models that normally need hundreds of GB of memory now run on consumer high-end PCs.

In this article, we explain KTransformers' mechanism and everything you need to get started, in beginner-friendly terms.

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What You'll Learn

• What KTransformers is (the mechanism)
• Why "giant AI on low-spec PCs" works
• Required hardware (CPU, GPU, memory)
• Installation and how to run it (overview)
• Recommended setups

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What Is KTransformers?

KTransformers is an open-source framework developed by Tsinghua University's MADSys Lab and others that accelerates LLM (large language model) inference by collaborating CPU and GPU.

In simple terms, it's the technology of "allocating AI computation to wherever GPU and CPU are each best."

Why It's Impressive

Giant MoE (mixture-of-experts) models only use a fraction of their parameters each time. KTransformers:

• Frequently used experts (hot) → placed in GPU memory
• Rarely used experts (cold) → placed in CPU memory (RAM)

This "smart allocation" lets you save GPU VRAM while running giant models.

Track Record (official benchmarks)

• Runs DeepSeek-R1 / V3 on 24GB VRAM (normally needs hundreds of GB)
• Up to 3–28× speedup
• In 2026, supports DeepSeek-V4-Flash, GLM-5.2, MiniMax-M3, etc. from "Day 0" (announcement day)

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Required Hardware

Here's the minimum hardware to run KTransformers.

Key point: As long as GPU VRAM is sufficient, more RAM lets you run bigger models. Even space-saving mini-PC setups can work with some ingenuity.

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Installation Overview

The official Quick Start is very simple.

cd kt-kernel
pip install .

Then prepare the quantized weights of a supported model (DeepSeek-V3/R1, Kimi-K2, GLM-5, etc.) and specify in the config file "which experts go to GPU and which to CPU."

See the official docs (kvcache-ai.github.io/ktransformers) for detailed steps.

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FAQ

Q1. Is it free?

Yes, KTransformers is open source (free). Run it freely on your own PC.

Q2. Does it work on Mac?

The CPU backend (AVX2 etc.) is supported, but GPU optimization is mainly for NVIDIA (CUDA) and Intel/AMD. Mac (Apple Silicon) support may be limited.

Q3. Can it run with 8GB VRAM?

Small models are possible, but for DeepSeek-R1-class models, 24GB+ VRAM is realistic. The design covers a lot with RAM, but you still need a decent GPU.

Q4. Can I fine-tune too?

Yes, SFT (supervised fine-tuning) is possible via LLaMA-Factory integration. There's a track record of fine-tuning DeepSeek-V3 on 4× RTX 4090.

Q5. Is it difficult?

If you can use Python and pip, it's basically just pip install. Model preparation and config need some learning, but the official tutorials are comprehensive.

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Summary

KTransformers is a revolutionary framework that realizes "giant AI on 24GB VRAM" through CPU-GPU collaboration.

• Open source developed by Tsinghua University and others
• Low-VRAM via smart hot/cold expert allocation
• DeepSeek-R1-class models run on 24GB VRAM (up to 28× faster)
• What you need: "24GB VRAM GPU + large RAM"

For anyone serious about local AI, this is one of the most important tools to check in 2026. Start by checking your PC's specs.

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