Ctrl K
Contact
Support · Kimi

Will it last?

Ownership levelPartialnone·limited·partial·substantial·fullAnalytical input C ยท 62.8/100

This page is a projection of the one entry record, the Transparency factor that Support covers. The full verdict is set by all four factors together, floor-weighted so the weakest caps the whole.

Which domain expands which factor
  • AssessUse & modify + Transparency
  • ImplementData control + Doesn't fail you
  • UseDoesn't fail you
  • SupportTransparency

Common problems & fixes

Aggregated common pitfalls (not an exhaustive catalogue):

  • Out-of-memory / sharding failures. The 1T model must be sharded across a multi-GPU/multi-node cluster; size tensor/pipeline parallelism to your hardware, or use KTransformers CPU-offload.
  • Block-FP8 kernel or engine errors. Match your vLLM/SGLang/TensorRT-LLM version to one that supports the block-FP8 weights and MLA attention; version mismatches are a common cause.
  • Garbled or over-verbose Instruct output. You are almost certainly not applying the chat template - use apply_chat_template, and don't prompt the Base checkpoint as a chat model.
  • Non-reproducible results / silent updates. You floated on main; pin an exact revision and verify checksums.

Versions, changelog & cadence

Kimi K2 ships as Base and Instruct checkpoints on the verified moonshotai org, tracked as immutable Hugging Face revisions - the revision hash is your changelog anchor: pin it, and diff against a newer revision when you choose to upgrade. Moonshot's "Kimi K2: Open Agentic Intelligence" technical report accompanies the release and documents what the model is. (Any newer Kimi variants beyond K2 are out of scope for this entry until independently verified.)

Community & support channels

  • Hugging Face model discussion tabs on the moonshotai org (ev-kimi-hf) for usage questions.
  • GitHub issues on moonshotai/Kimi-K2 (ev-github) for deployment, serving-engine, and reproduction problems.

There is no paid support tier for the open weights - this is community and maintainer support around an open-weight release.

Tracked known issues

Drawn from model-card and third-party caveats rather than a formal issue tracker (hence partial):

  • China-aligned topic censorship on politically sensitive prompts.
  • Lighter safety coverage than frontier labs, with no companion guard model.
  • Operational burden of a 1T-parameter model - multi-node infrastructure is required with no small fallback variant.

How this scores

The ownership factor this domain covers, drawn from the one entry record.

2

TransparencyDo you know what it is: weights, training, behaviour, and legible terms?

Weak

You cannot see what the model is: the technical report documents a 1T/32B MoE trained on 15.5T tokens with MuonClip, but the training corpus and training code are closed, and the weights carry China-aligned topic censorship you cannot inspect.

How this scores (AOI sub-dimensions)
Provenance3/5how well we can trace and verify what went into the modelDistributed from the verified moonshotai org on Hugging Face as block-FP8 safetensors with checksums and no canonical-org malicious incident on record (checklist 5/8).
Governance3/5how accountable and well-documented the publisher isActive, accountable publisher (Moonshot AI) with a detailed technical report and a verified hub presence, but no documented vulnerability-disclosure or deprecation policy and no EU Code of Practice signature.
What this means for adoptionYou substantially use, modify and commercialise the self-hosted Kimi K2 weights under a near-MIT grant - the sole condition is the verbatim 100M-MAU / $20M-revenue trigger to display 'Kimi K2' in your UI - and run entirely on your own infrastructure, so your data stays yours. What holds ownership at partial is transparency: the 15.5T-token corpus and training code are closed and the model carries China-aligned censorship you cannot inspect. Mind the weights-vs-hosted split - the platform.kimi.ai API trains on your content by default (opt-out only by enterprise agreement, Singapore law), so self-host if data control matters. Confirm the attribution trigger and the per-checkpoint LICENSE before shipping, and budget multi-node infrastructure to run it at all.

Sources

The same evidence records as the entry sheet. Read means the text was verified; unverified means it is known to exist but not yet read.

Model cardread2026-07-25
Kimi-K2-Instruct model card on the verified moonshotai Hugging Face org: block-FP8 safetensors, 1T total / 32B active parameters, 128K context, Muon optimiser, and the statement that "Both the code repository and model weights are released under the Modified MIT License."
Licenceread2026-07-25
Kimi K2 LICENSE, read verbatim: standard MIT with one added clause - "Our only modification part is that, if the Software (or any derivative works thereof) is used for any of your commercial products or services that have more than 100 million monthly active users, or more than 20 million US dollars (or equivalent in other currencies) in monthly revenue, you shall prominently display 'Kimi K2' on the user interface of such product or service." Otherwise standard MIT.
Documentationunverified2026-07-25
The moonshotai/Kimi-K2 repository documents the MoE architecture (384 experts, 8+1 selected), the MuonClip optimiser, block-FP8 weights and deployment on vLLM, SGLang, KTransformers and TensorRT-LLM with an OpenAI/Anthropic-compatible API.
Technical_reportread2026-07-25
Moonshot's arXiv technical report "Kimi K2: Open Agentic Intelligence" (2507.20534), read: a 1T-total / 32B-active MoE trained on 15.5T tokens with the MuonClip optimiser (Muon + QK-clip), reporting agentic/coding results (e.g.
Terms of serviceread2026-07-25
platform.kimi.ai model-use agreement, read: user content is used to improve the services, with opt-out available only via an enterprise or separate written agreement; governed by Singapore law with disputes resolved by SIAC arbitration in English.
Privacy Policyread2026-07-25
platform.kimi.ai privacy policy, read: the hosted service trains on user prompts, audio, images, videos and files by default; the controller is MOONSHOT AI PTE.
Third-party analysisunverified2026-07-25
On independent evaluation Kimi K2 is among the strongest open-weight models on agentic and coding benchmarks (e.g.
Third-party analysisunverified2026-07-25
Independent analysis notes Kimi K2, like other China-based open-weight models, applies China-aligned content filtering on politically sensitive topics.
Third-party analysisunverified2026-07-25
Kimi K2 Instruct is safety-tuned but with lighter alignment coverage than Western frontier labs and no companion guard model.
Third-party analysisunverified2026-07-25
No public EU AI Act training-content summary, copyright policy, or provider documentation package is published for Kimi K2, and the training corpus is not released.
Third-party analysisunverified2026-07-25
Kimi K2 is served across vLLM, SGLang, KTransformers and TensorRT-LLM, but its 1T-parameter scale requires multi-GPU / multi-node infrastructure even in block-FP8.