Kimi K3 by Moonshot AI is a 2.8T parameter open-weight model (104B active) built for SOTA coding, agentic, long-context, and chat workloads. It is the strongest open model to date, rivaling Claude 4.8 Opus and GPT-5.6. Kimi K3 has native vision, a 1M-token context window and uses MXFP4. Full-precision inference requires 1.56 TB of storage and 1-bit Kimi K3 Unsloth Dynamic GGUF requires 594 GB (62% less).

Dynamic 1-bit (see right) reaches ~78.9% top-1 accuracy while being 62% smaller. Dynamic 2-bit 861.3GB reaches ~90% accuracy while being 45% smaller. Run Kimi-K3-GGUF via Unsloth Studio or llama.cpp. Kimi K3 can run on a NVIDIA DGX Station, or Mac Studio connected to a 128GB RAM device.

For lossless Kimi K3, use Q8 (UD-Q8_K_XL), which is 50GB larger than Q4 (UD-Q4_K_XL). We are still investigating if we can push it under 512GiB (dynamic 1-bit is 553.2 GiB) without damaging the model.

Run Kimi K3 TutorialsQuant Results

Table: Hardware requirements (units = total memory: RAM + VRAM, or unified memory)

Kimi K3 GGUF Implementation Details

We built on top of llama.cpp PR with our fork which includes vision support and some bug fixes.

1. The mmproj / vision tower is similar to the Kimi-K2.5 tower, but with RMSNorm, no biases, a non-square fused QKV (qkv width != n_embd) and a post-norm projector.

2. We found when running llama.cpp, the n_tokens * 40 budget failed at large batch sizes, and so we had to up to n_tokens * 160

3. We also had to convert the Kimi chat template to a jinja format.

4. We tested as much as we could to cover all cases. Kimi by default has been trained with preserved thinking on, so all thinking traces are not deleted, but kept.

📊 Quantization Analysis

Like Kimi K2.6 and K2.7, K3's UD-Q8_K_XL is lossless because Kimi uses MXFP4 for MoE weights and BF16 for everything else, and Q8_K_XL follows that exactly. UD-Q4_K_XL is similar except some of remaining tensors (except norms etc) are Q8_0, so it is near full precision and requires 1.56 TB RAM/VRAM. UD-Q8_K_XL is 'truly lossless' vs the MXFP4 full safetensors version.

For imatrix generation and quantization, we used the 1.56 TB lossless UD-Q8_K_XL throughout calibration; its perplexity is 1.4581. Our Dynamic-1bit quant reaches 2.58 perplexity with 79% top-1 accuracy, making it surprisingly usable.

Other community quants are larger yet degrade far more. For example, one 618.9 GB quant IQ1_M exceeds our 594 GB 1-bit quant, but its perplexity jumps to 54.56 - 21× worse. The same pattern holds for IQ2_XXS: 725 GB at 96 PPL vs. our 711 GB at 2.12 PPL - 45× worse, meaning their 2-bit performs even worse than their 1-bit. This highlights importance of dynamic quantization + proper calibration.

We also provide Top-1% Accuracy, KLD plots:

⚙️ Usage Guide

Kimi K3 is thinking-only, with preserve_thinking always enabled and max thinking on by default. Instant mode is not supported. Thinking effort is configured with the reasoning_effort request field, and K3 supports "low", "high", and "max" thinking efforts.

• Context length = up to 1,048,576

• Low, High, Max Thinking is toggagle in Unsloth

If the model fits, you will get ~20 tokens/s generation when using B200s and >120 tokens / s throughput. We recommend UD-IQ1_S (594GB) as a good size/quality balance. Best rule of thumb: RAM+VRAM ≈ the quant size; otherwise it’ll still work, just much slower due to disk offloading.

Run Kimi K3 Guide

You can now run Kimi K3 in llama.cpp and Unsloth Desktop. We will be utilizing the 594GB UD-IQ1_S quant for best results in terms of accessibility and accuracy and it will require at least 610GB RAM. Feel free to change quantization type. GGUF: Kimi-K3-GGUF

🦥 Run Kimi-K3 in Unsloth

Kimi K3 can run in Unsloth Desktop, an open-source desktop UI for local AI. Unsloth Desktop automatically offloads to RAM and detects multiGPU setups. With Unsloth Studio, you can run models locally on MacOS, Windows, Linux and:

• Search, download, run GGUFs and safetensor models

• Self-healing tool calling + web search

• Code execution (Python, Bash)

• Automatic inference parameter tuning (temp, top-p, etc.)

• Fast CPU + GPU inference via llama.cpp

• Train LLMs 2x faster with 70% less VRAM

1

Install and Launch Unsloth

The easiest way to get started is by downloading the Unsloth Desktop app. Works on macOS, Windows, and Linux.

Download Unsloth

• Download for macOS

• Download for Windows

• Download for Linux

Or, if you prefer to install manually:

MacOS, Linux, WSL:

Windows PowerShell:

Launch Unsloth

MacOS, Linux, WSL and Windows:

Then open http://127.0.0.1:8888 (or your specific URL) in your browser.

Launch Unsloth securely with HTTPS and Cloudflare

NEW! Unsloth now provides a secure way to launch Unsloth over HTTPS through a free Cloudflare tunnel. Use the below (works in Windows, Mac & Linux):

2

Search and download Kimi K3

Unsloth Studio automatically offloads to RAM and detects multiGPU setups. On first launch you will need to create a password to secure your account and sign in again later.

Then go to the Model hub tab and search for Kimi K3 in the search bar and download your desired model and quant. Ensure you have enough compute the run the model.

3

Run Kimi K3

Inference parameters should be auto-set when using Unsloth Studio, however you can still change it manually. You can also toggle low, high or max thinking, edit the context length, chat template and other settings.

For more information, you can view our Unsloth Studio inference guide.

🦙 Run Kimi K3 in llama.cpp

For these tutorials, we will use llama.cpp for fast local inference, especially if you have a CPU. We created a fork specifically to support Kimi K3 vision thus. This builds on top of another llama.cpp PR.

1

Obtain the SPECIFIC Unsloth fork of llama.cpp on GitHub here to enable vision support. You can follow the build instructions below as well. Change -DGGML_CUDA=ON to -DGGML_CUDA=OFF if you don't have a GPU or just want CPU inference. For Apple Mac / Metal devices, set -DGGML_CUDA=OFF then continue as usual - Metal support is on by default.

2

Let's first get an image! You can also upload images as well. We shall use this image, which is just our mini logo showing how finetunes are made with Unsloth:

Let's get the 2nd image here

3

You can now use llama.cpp directly to load and download models, just like ollama run. First, select the quantization type you want like IQ1_S. Also use export LLAMA_CACHE="folder" to force llama.cpp to save to a specific location. Note this download process might be very slow, so it's probably best to use the manual download process in the next section.

4

If you want to download the model manually, we can download the model via the code below (after installing pip install huggingface_hub). If downloads get stuck, see: Hugging Face Hub, XET debugging

5

Then run the model in conversation mode:

6

You will then see:

And I asked "What is the sqrt of -1":

Kimi K3 also supports images for eg loading the Unsloth image:

And then we use the sloth image and ask how it's related:

📊 Benchmarks

You can view further below for benchmarks in table format:

DeepSWE Benchmarks show Kimi-K3 doing very efficiently!