Can You Run Kimi K3.1 Pro Locally? The 1.56 TB Answer

Not today. Kimi K3.1 Pro's official weights total about 1.56 TB, which is more memory than any Mac or single GPU offers, and the two local engines most desktop setups rely on, llama.cpp and mlx-lm, do not support its new kimi_k31 architecture yet. This short guide gives the sourced numbers and what to use instead.

TL;DR: Kimi K3.1 Pro is a 2.8T-parameter mixture-of-experts model with 104B active parameters, per the official model card. Its MXFP4 weights take 1,561,217,377,280 bytes, about 1.56 TB, per the safetensors index. llama.cpp and mlx-lm cannot load it yet, and no GGUF exists.

What are Kimi K3.1 Pro's specs?

Every figure below links to its source on Hugging Face.

The published checkpoint is already 4-bit MXFP4, so there is no larger release to shrink: the 1.56 TB download is the compact version.

How much memory does Kimi K3.1 Pro need?

More than 1.56 TB for the weights alone, before context cache, runtime buffers and the operating system. That is our arithmetic on the index file's total_size, not a test. The largest Mac Studio tops out at 512GB of unified memory, per Apple's Mac Studio tech specs, so the weights alone are about 2.8 to 3 times what it holds, before KV cache and the operating system: 1,561,217,377,280 bytes is 3.05 times 512 GB counted in decimal (512,000,000,000 bytes) and 2.84 times 512 GiB (549,755,813,888 bytes), our arithmetic. It is also far beyond any single GPU. We publish no speed figure, because no local engine can load the model to produce one.

Do llama.cpp and mlx-lm support Kimi K3.1 Pro?

No, not as of November 5, 2026. Kimi K3.1 Pro declares a new architecture, kimi_k31, and the model card says it replaces Kimi K3's full-attention Gated MLA layers with Gated DeepSeek Sparse Attention.

• llama.cpp: its converter conversion/kimi_k3.py registers only KimiK3ForConditionalGeneration, so Kimi K3.1 Pro cannot be converted to GGUF with upstream tools.
• mlx-lm: mlx_lm/models has kimi_k3.py but no kimi_k31 model file.

A Hugging Face search with the gguf filter returns no build. Moonshot's card recommends server engines instead: vLLM, SGLang and TokenSpeed.

How does it compare with Kimi K3?

Kimi K3 is the same size class and does have llama.cpp and mlx-lm support, yet it still does not fit a Mac at a quantization we recommend. Our Kimi K3 VRAM guide lists its real file sizes, the Kimi K3 local build guide covers the server it takes, and the Kimi K3 model page shows the specs. Kimi K3.1 Pro starts from the same scale with less software around it.

FAQ

Can a 512GB Mac Studio run Kimi K3.1 Pro?

No. The weights alone are about 1.56 TB, about 2.8 to 3 times its 512GB of unified memory, and mlx-lm does not support the model yet.

Is there a GGUF of Kimi K3.1 Pro?

No. The llama.cpp converter does not register the kimi_k31 architecture, and a Hugging Face search with the gguf filter returns nothing.

Can I still try Kimi K3.1 Pro from my computer?

Yes, as a hosted model: ollama run kimi-k3.1-pro:cloud, listed on Ollama's library page, runs on Ollama's servers rather than on your machine.

Sources

• moonshotai/Kimi-K3.1-Pro model card, config.json, model.safetensors.index.json and LICENSE, read November 5, 2026
• llama.cpp conversion/kimi_k3.py and mlx-lm models directory, read November 5, 2026

Every number in this article is read from the source linked beside it or is our own arithmetic, labelled as such. How we work: about ModelFit. Corrections welcome: contact.