Can You Run Kimi K3.1 Locally? Same 2.8T K3 Shape, Weights Due Nov 4

Last updated: October 2026

Not yet, and when the weights land on or before November 4, 2026, still not on a computer you own. Kimi K3.1 Pro is a post-trained Kimi K3 with the same 2.8 trillion total and 104 billion active parameters, so the hardware answer this page worked out for K3 in July carries over unchanged: a 600GB-class 1-bit file and a floor of 610GB of combined RAM and VRAM. What changed is the model's quality and price in the cloud.

This page has tracked one question through four phases: the K3 launch on July 16, the K3 weights on July 27, Unsloth's calibrated 1-bit quants on July 29, and now K3.1 Pro on October 23. Here is what Moonshot released, what it means for a home machine, and what to run instead.

What Moonshot Released on October 23

K3.1 Pro is live today on hosted surfaces only: Kimi.ai, the Kimi mobile apps, Kimi Work, Kimi Code through the /model command, and the Kimi API as kimi-k3.1-pro and kimi-k3.1-pro-highspeed, the same model at two to three times the output speed. There is no smaller variant, no distilled version, and no Code-only release of the kind K2.7-Code was.

The headline facts come from Moonshot directly. Its launch announcement reads "1M-token context, 2.8T total / 104B active params (MoE), Multimodal," calls K3.1 Pro "our most capable 3T-class open-weight model," and closes with "Open weights by Nov 4, 2026." The technical blog post adds that the model was post-trained from K3 with substantially more compute, and the API quickstart states that K3.1 Pro always runs in thinking mode, with a reasoning_effort of low, high or max.

K3 figures from Moonshot's model card, July 27, 2026. K3.1 Pro figures from Moonshot's launch announcement and blog post, October 23, 2026. Internal layer and attention layout, and exact file sizes, stay unverified until the config file and weights ship. No K3.1 repository existed under Moonshot's Hugging Face organization that day.

Our breakdown of the Kimi K3 benchmark claims covers how to read Moonshot's charts.

Why the Hardware Answer Has Not Moved

Same parameter count and the same active width mean the same file sizes at every precision. The only thing that could change the home-hardware verdict is a smaller official build or a distilled sibling, and Moonshot announced neither. So the K3 quantization ladder is the best forecast for K3.1, with one caveat: these are Unsloth's measured K3 files, and the K3.1 files do not exist yet.

Sub-4-bit is still only reachable through calibrated dynamic quantization. K3 was trained quantization-aware and ships natively at 4-bit, so the compression that normally shrinks an open model after release was spent before it. Unsloth drops most weights to 1 or 2 bits while upcasting the layers that matter most to 8-bit, calibrated against a lossless reference. The ladder below is Unsloth's current K3 guide, which has settled since the July revisions this page first reported.

Unsloth-reported K3 figures, read October 23, 2026. The 1-bit floor has settled at 610GB, down from the 650GB-plus guidance Unsloth published on July 29. Expect K3.1 files within a few percent of each row once calibrated builds exist.

Three hardware paths clear a 610GB floor, and none is a normal computer. A 512GB Mac Studio alone does not reach it; Unsloth's documented workaround pairs it with a second 128GB machine over llama.cpp's network mode for 640GB of pooled memory, which now clears the IQ1_S floor with a little room. A retired EPYC or Xeon server with 768GB or more of RAM will load the file for CPU-only inference at speeds that make it a demonstration rather than a tool. Datacenter accelerators are where Unsloth's quoted 100-plus tokens per second come from. Where server-class economics begin to make sense is covered in our local AI hardware guide.

One consumer detail: a 594GB download is roughly half of a typical 1.2TB monthly data cap. If your ISP meters you, our estimate of how long the Kimi K3 download takes on your plan applies to K3.1 at the same sizes.

What You Can Use Today, and What Leaves Your Machine

Every way to use K3.1 Pro right now is hosted. The Kimi app, Kimi Work, Kimi Code and the API all send your prompts, files and code to Moonshot's servers. Moonshot's launch post advertises Zero Data Retention for API customers, which is a real guarantee and a hosted-only one: it governs what Moonshot keeps, not where inference happens. The consumer app runs on different terms, and our companion piece on what you actually pay with when Kimi is free is the more urgent read before you paste anything sensitive.

A dated weights commitment is a promise, and K3's July 27 release shows Moonshot keeps them. The blog post is also candid that the open checkpoint is deliberately not the strongest one it trained: Moonshot says it removed cyber-related data from both training stages before the open release, and reports the pre-mitigation checkpoint scoring 90.5 percent on its exploit benchmark against 81.0 for the released model. That is a defensible call, and the first time Moonshot has described a release as held back on purpose, so read the model card when it lands rather than assuming it matches the hosted product.

Runtime Support: Confirmed for K3, Unverified for K3.1

Since K3.1 Pro shares K3's architecture, the runtime picture should carry over, but every line below is a K3 fact until K3.1 files exist and someone converts them.

llama.cpp. Text support for the Kimi-K3 architecture is in mainline: the "add Kimi-K3 text model" pull request merged on August 15, 2026, so the July-era dedicated branch is no longer required for text. Vision is a separate requirement. K3.1 Pro is multimodal, and image input needs an mmproj projector that mainline does not carry for this model; Unsloth's guide still points at its own fork and a kimi-k3-fullsize-vision branch. Local K3.1 vision waits on that work landing upstream.

Ollama and LM Studio. The Ollama Kimi K3 listing still carries one tag, kimi-k3:cloud, as of October 23: requests route to Ollama's servers on a paid tier, a hosted API wearing a familiar command. LM Studio offers K3 only as a cloud model in its Bionic tier. Neither has a local build, and neither lists K3.1.

vLLM and SGLang. Both supported K3 from launch day and are what Moonshot's model card names for serving: multi-GPU server tools, irrelevant to a desktop with one card.

Before November 4, nothing downloadable is K3.1. Any Hugging Face repository, Ollama tag or torrent claiming to be local Kimi K3.1 weights before Moonshot publishes them is a relabeled K3 file, a wrapper that calls the hosted API, or a counterfeit. The calendar is the authenticity check. Once the weights drop, July's lesson applies: wait for importance-matrix-calibrated 1-bit and 2-bit builds from a quantizer that could run the model to calibrate them, not day-zero conversions done blind. Unsloth's K3 calibration put its 1-bit build at roughly 79 percent agreement with the full model on token choices; an uncalibrated conversion publishes no such number because it cannot.

The License Question

K3 did not ship under MIT or Apache 2.0. It uses a bespoke Kimi K3 License that reads MIT-like until two gates: model-as-a-service businesses need a separate Moonshot agreement once revenue across the business and its affiliates passes 20 million US dollars over any twelve consecutive months, and a commercial product above 100 million monthly active users or 20 million US dollars in monthly revenue must display "Kimi K3" prominently in its interface. Neither gate touches internal use or a homelab.

K3.1 Pro's license is not stated anywhere in Moonshot's launch materials, and a point release is exactly where terms can quietly change. Do not assume the K3 terms carry over. Read the LICENSE file when the repository appears, check whether the revenue and attribution gates moved, and have counsel confirm your case before building anything commercial on it.

What to Run Instead

Moonshot's launch chart is the most useful thing in the release for a home user, because of who it benchmarks against. Alongside Claude Opus 5.5, Claude Fable 5.1 and GPT-6 Astra, it lists two open models, GLM-5.3 and DeepSeek-V4.1-Flash-1017, and one of those runs on prosumer hardware. These are Moonshot's numbers at maximum thinking effort; the chart's footnote says the Claude results may include fallbacks and the GPT results may include cyberguards, so treat it as a vendor's chart, not an audit.

Scores from Moonshot's K3.1 Pro launch chart, October 23, 2026, all models at maximum thinking effort. Independent evaluations had not published a K3.1 Pro entry we could check at this update; Artificial Analysis and LMArena figures will be added when they exist.

Read the gap honestly. K3.1 Pro leads DeepSeek V4.1-Flash by 10 to 24 points on every row, so it is the better model by Moonshot's own account, and it costs hosted-model money: Moonshot's pricing page listed K3.1 Pro at 2 US dollars per million input tokens and 10 per million output on launch day, and its Terminal-Bench cost curve puts the model near 5 dollars per task against roughly 14.50 for Opus 5.5. DeepSeek V4.1-Flash sits under a dollar a task on the same curve, and it is the one you can own. Its 1017 weights run on two DGX Sparks, a 256GB to 512GB Mac Studio, or an EPYC rig with the 1-bit file; our DeepSeek V4.1-Flash reality check has measured speeds and file sizes for each path. That is the local baseline Moonshot chose to compare itself against, and the right one for a reader with real hardware.

Below that tier, open models in the 8GB to 128GB range handle coding, summarization, document work and light agent tasks well. Our guide to the best local AI models by VRAM tier maps the strongest pick at every memory level, and the mini PC guide for local AI covers an always-on box and how to keep it isolated on your network.

Not sure where your machine lands? Our LLM VRAM calculator grades Kimi K3 against your graphics card, Mac or mini PC, names the file to download and prints the command to start it; the K3 entry is the K3.1 forecast until K3.1 files exist. It runs in your browser, with no sign-up.

Check your hardware for Kimi K3

Frequently Asked Questions

Can you run Kimi K3.1 locally?

Not yet, and not on consumer hardware when the weights arrive. Moonshot released K3.1 Pro on October 23, 2026 as a hosted model and promised open weights by November 4. It shares K3's 2.8 trillion total and 104 billion active parameters, so expect the same floor: about 610GB of combined RAM and VRAM for a calibrated 1-bit build, which no single desktop or Mac reaches.

When do the Kimi K3.1 weights come out?

Moonshot's launch announcement says "Open weights by Nov 4, 2026." As of October 23 there was no K3.1 repository under Moonshot's Hugging Face organization. Anything claiming to be downloadable K3.1 weights before Moonshot publishes them is a relabeled K3 file, an API wrapper, or a fake.

Is Kimi K3.1 the same size as Kimi K3?

Yes. Moonshot states 2.8 trillion total parameters, 104 billion active per token, a 1 million token context and multimodal input for K3.1 Pro, the same figures as K3. It is a post-trained K3, not a new architecture, so Unsloth's K3 quantization ladder, from a 594GB 1-bit file to a 1,560GB 8-bit file, is the best forecast for K3.1 file sizes.

Does Ollama or LM Studio run Kimi K3.1?

No. Ollama's Kimi K3 listing has one tag, kimi-k3:cloud, which routes requests to Ollama's servers on a paid tier; LM Studio offers K3 only as a cloud model in its Bionic tier. Neither has a local build of K3 or lists K3.1. Local inference means mainline llama.cpp for text, with vision still on an Unsloth fork.

What license will Kimi K3.1 use?

Moonshot has not said. K3 shipped under a bespoke Kimi K3 License that behaves like MIT until a business passes 20 million US dollars in twelve-month model-as-a-service revenue or a product passes 100 million monthly users, which triggers a separate agreement or prominent attribution. Do not assume those terms carry over; read the LICENSE file when the K3.1 repository ships.

What can I run at home instead?

DeepSeek V4.1-Flash-1017 is the open model in Moonshot's own K3.1 chart that fits prosumer hardware: two DGX Sparks, a 256GB to 512GB Mac Studio, or an EPYC server with the 1-bit file. Below that, open models from 8GB to 128GB cover coding, summarization and document work well; our VRAM-tier guide and the LLM VRAM calculator name the best pick for your memory.