Kimi K2.6 is an open model by Moonshot that delivers SOTA performance across vision, coding, agentic, long context and chat tasks. The 1T-parameter hybrid thinking model has 256K context length and full precision requires 610GB of disk space Dynamic 2-bit requires 350GB (-43% size). Run Kimi K2.6 via Unsloth Dynamic Kimi-K2.6-GGUFs on Unsloth Studio or llama.cpp.

Dynamic 2-bit upcasts important layers to 8-bit and needs 350GB+ VRAM/RAM setups. For lossless Kimi K2.6, use Q8 (UD-Q8_K_XL), which is only 10GB larger than Q4 (UD-Q4_K_XL). All uploads use Dynamic 2.0 for SOTA quantization performance. Kimi-K2.6 GGUFs also support vision.

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

📊 Quantization Analysis

UD-Q8_K_XL is lossless because Kimi uses int4 for MoE weights and BF16 for everything else, and Q8_K_XL follows that. UD-Q4_K_XL is similar except the remaining tensors are Q8_0, so it is near full precision and requires 600GB RAM/VRAM. Other non-Unsloth GGUFs from other providers may follow the UD-Q4_K_XL approach rather than the 'truly lossless' UD-Q8_K_XL.

We followed jukofyork's finding that const float d = max / -7; instead of the default const float d = max / -8; during the quantization process only on the MoE layers. This bijection patch on INT4-native MoEs allows the Q4_0 quant-type to reduce absolute error from 1.8% to near 0% (epsilon).

However we must keep other layers in BF16, and show below the error plots for both versus the BF16 baseline. UD-Q8-K_XL is truly "lossless" with some machine epsilon difference when converting Q4_0 to BF16. The perplexity for UD-Q8_K_XL was 1.8419 ± 0.00721 and UD-Q4_K_XL 1.8420 ± 0.00720. Note the error plot below is RMSE divided by bfloat16 epsilon, so it's a small error scale.

⚙️ Usage Guide

Thinking and non-thinking mode require different settings:

• Suggested context length = 98,304 (up to 262,144)

If the model fits, you will get >40 tokens/s when using B200s. We recommend UD-Q2_K_XL (350GB) as a good size/quality balance. Best rule of thumb: RAM+VRAM ≈ the quant size; otherwise it’ll still work, just slower due to offloading.

Chat Template for Kimi K2.6

Running tokenizer.apply_chat_template([{"role": "user", "content": "What is 1+1?"},]) gets:

Run Kimi K2.6 Guide

🦥 Run Kimi-K2.6 in Unsloth Studio

Kimi K2.6 can run in Unsloth Studio, an open-source web UI for local AI. Unsloth Studio 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

To install, run in your terminal:

MacOS, Linux, WSL:

Windows PowerShell:

Launch Unsloth

MacOS, Linux, WSL and Windows:

Then open http://localhost:8888 in your browser.

2

Search and download Kimi-K2.6

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 Studio Chat tab and search for Kimi-K2.6 in the search bar and download your desired model and quant. Ensure you have enough compute the run the model.

3

Run Kimi-K2.6

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

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

🦙 Run Kimi K2.6 in llama.cpp

For this guide we'll be running the UD-Q2_K_XL quant which will require at least 350GB RAM. Feel free to change quantization type. GGUF: Kimi-K2.6-GGUF

For these tutorials, we will using llama.cpp for fast local inference, especially if you have a CPU.

📊 Benchmarks

You can view further below for benchmarks in table format: