Can I Run Kimi K3.1 Pro Locally? (2026): No, 1.56 TB

Bottom Line

• Can I run Kimi K3.1 Pro locally? No. The Hugging Face repo is 1.56 TB across 96 safetensors shards. 2.8T total parameters, 104B active per token.
• Quantization will not save you. The weights are already MXFP4 with MXFP8 activations, trained that way. A 2-bit cut would still be about 780 GB, derived by halving, and no such file exists.
• No GGUF, no llama.cpp. As of 4 November 2026 the Hugging Face search returns no GGUF repo. The card recommends vLLM, SGLang and TokenSpeed.
• One desk-class experiment exists. Inferencer Labs posted a 220 GB MLX repack that offloads the experts to storage, tested on an M3 Ultra 512GB. Its speed field reads ”~ tokens/s”. Blank.
• Run instead: Qwen3.8 27B at 24GB, Ornith 1.5 122B or gpt-oss 120B at 128GB, GLM-5.3 at 512GB. Numbers below.
• Same size as Kimi K3. The K3 page from July still holds. What changed is the attention stack, the benchmarks, and a 512GB Mac that Apple now sells again.

Ready to buy? See the tested hardware list with current prices.

What Kimi K3.1 Pro Is

Read from the model card on 4 November 2026.

Why it trends. The weights landed on 4 November, the date Moonshot set at the 23 October launch. The release trended on X the same day. By the evening, Hugging Face listed 18 repos with the name, including an Unsloth mirror, two DSpark draft models, expert slices, and the MLX repack.

The Arithmetic: Why It Cannot Fit

Start from the repo, not from the parameter count. The file tree on Hugging Face totals 1.56 TB. The Unsloth mirror is the same 1.56 TB of safetensors.

A 1T-class BF16 checkpoint usually shrinks 4x at 4 bits. K3.1 Pro already made that move. Moonshot trained it with MXFP4 weights from the quantization-aware stage, so the released file is the squeezed version. Halve it again to a hypothetical 2-bit build and you get about 780 GB. That number is derived, no such file exists, and 2-bit would damage the tool-calling the model is sold on.

The last row is how the DSpark team evaluated the model: “one 8x B300 node, tensor parallel 8, FP8 KV cache.” A community estimate on X priced that server at $550,000 to $650,000. We did not verify the price.

The 220 GB MLX Cut, Honestly

Inferencer Labs published inferencerlabs/Kimi-K3.1-Pro-MLXse within hours of the release. The card says the build “repacks the model’s existing quantized weights and offloads the experts to storage for running at full precision on memory constrained devices.” The size box reads 220 GB at 4-bit. It was “Tested with Inferencer 2.5.2” on an “M3 Ultra 512GB”, and the speed line reads ”~ tokens/s” with no number.

What that means in practice:

• The 220 GB is the resident part. The experts live on the SSD and stream in per token. Every token touches 16 of 896 experts, so the disk, not the memory bus, sets the speed.
• No one has published a tokens-per-second figure. Not the author, not a reviewer. We do not estimate one.
• It needs a modified MLX. The card says “Repacked with a modified version of MLX” and installs mlx-vlm. Stock LM Studio support is a sidebar link, not a tested claim.
• It runs inside 512GB only. A 256GB Mac Studio does not hold the 220 GB resident set plus a usable context.

If a measured number appears, this section changes. Until then, do not buy a $22,299 Mac Studio to run K3.1 Pro.

What to Run Instead

Every file below is a real download that loads in stock llama.cpp. Sizes come from our tier pages, which read them from the Hugging Face API.

For the 24GB row, a used RTX 3090 had a lowest ask of about $1,500 on GPU Dojo as of November 2026. For the 128GB rows, the NVIDIA DGX Spark lists at $6,950 as of October 2026, and the ASUS Ascent GX10 is the cheaper GB10 box.

The honest comparison: K3.1 Pro scores 65.7 on Terminal-Bench 4.0 against Claude Opus 5.5 at 66.4, by Moonshot’s own table. None of the models in the table above gets near that. They run on a desk. K3.1 Pro runs on a node.

Benchmarks Moonshot Published

Vendor numbers from the card, “max” reasoning effort. Several use internal or custom harnesses, as the card’s footnotes say.

There is still no SWE-bench Verified number for the K3 family. If you see one quoted, ask where it came from.

The License, in Three Lines

1. Commercial use is allowed. The grant covers use, copy, modify, distribute, sublicense and sell, with the copyright notice kept.
2. Model-as-a-Service above $20M in 12 months needs a separate agreement with Moonshot AI before commercial use.
3. Above 100M monthly users or $20M monthly revenue, show “Kimi K3.1-Pro” in the UI. Internal use and use through Moonshot’s own products are exempt.

Common Mistakes

1. Waiting for a GGUF. The weights are 4-bit already. A GGUF would be the same 1.5 TB, give or take the format overhead.
2. Reading “8-bit precision” on the Hugging Face tag as the weight format. The tag reflects the U8 storage tensors. The card says MXFP4 weights, MXFP8 activations.
3. Treating the DSpark repos as a small K3.1 Pro. They are 2.25B-parameter draft models for speculative decoding. They need the full model to run.
4. Buying 512GB for the MLX repack. The speed is unpublished. Buy 512GB for GLM-5.3 at 4-bit, which is a measured, normal load.
5. Assuming MIT. The license is MIT-derived with revenue and attribution clauses. Read it before you build a product on it.

Sources

• Model card, config and benchmark table: moonshotai/Kimi-K3.1-Pro (read 2026-11-04)
• Repo size and shards: file tree, 1.56 TB, model-00001-of-000096 and up (read 2026-11-04)
• License text: LICENSE (read 2026-11-04)
• GGUF search: huggingface.co/models?search=Kimi-K3.1-Pro GGUF, no results on 2026-11-04
• MLX repack: inferencerlabs/Kimi-K3.1-Pro-MLXse (read 2026-11-04)
• Unsloth mirror: unsloth/Kimi-K3.1-Pro, 1.56 TB safetensors (read 2026-11-04)
• Draft model and the 8x B300 eval setup: LithosAI/Kimi-K3.1-Pro-DSpark (read 2026-11-04)
• Release coverage: Superpower Daily, 2026-11-04; AI/TLDR, which records the 23 October launch and the 4 November date; Andrew Curran on X; server cost estimate: Chubby on X, community figure, unverified
• Run-instead sizes: our 24GB, 128GB VRAM, 128GB RAM and 512GB pages
• RTX 3090 ask: GPU Dojo, 2026-11-03; DGX Spark price: The Register, 2026-10-02

See Also

• Can I Run Ripple-65B Locally? — the other 4 November release, a dense 65B that does fit 48GB
• Can You Run Kimi K3 Locally? — the July release, same 2.8T size, with the Laguna alternatives
• Kimi K3 vs DeepSeek V4 vs GLM 5.2 — how the frontier open models compare
• Best Local LLM for 512GB RAM — what a 512GB Mac Studio runs normally
• Can I Run Mistral Large 4 Locally? — the other 1T-class release, with byte-exact GGUF sizes
• Best Local LLM for 128GB VRAM — Ornith 1.5 122B and the 128GB boxes
• Best Local LLM for 24GB VRAM — Qwen3.8 27B on a 3090
• DGX Spark Price: Every GB10 Box — current prices for the 128GB tier

Before you order parts, check the tested hardware list for current prices by tier.