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Qwen3-MoE

Mixture-of-experts Qwen3 in bf16; expert weights and router rows are permuted under the key.

Official sealed: rolling outorthpermSGLangvLLM

Commands

Pull, seal and run

kllm pull downloads a library tag into the local store and records the hash of every file. kllm run starts SGLang or vLLM in the background on 127.0.0.1 and opens a chat. kllm create seals a pulled model under a named key.

# Download the library tag into the local store; a manifest records each file's hash
$ kllm pull qwen3-moe:30b-a3b
# Start SGLang or vLLM in the background on 127.0.0.1 and open a chat
$ kllm run qwen3-moe:30b-a3b
The engine server exposes an OpenAI-compatible API on 127.0.0.1. Stop it with kllm stop.

Requests to the running server

# The local proxy listens on 127.0.0.1:8001 and tokenizes on your machine
curl http://127.0.0.1:8001/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{"model": "Qwen3-30B-A3B-Instruct-2507-sealed", "messages": [{"role": "user", "content": "Hello"}]}'

Official sealed models

Pre-sealed under model keys

The kLLM team seals selected public models at a pinned revision under the perm transform, locks the weights and publishes them under ANetResearch on Hugging Face. Each model has its own model key, issued to approved applicants. The artifacts are rolling out; a repository is available once it appears in the organization.

Official sealed models of Qwen3-MoE
RepositoryBase and revisionModalityLicenceTransformClient-keyedStatus
Qwen3-30B-A3B-Instruct-2507-kllmQwen/Qwen3-30B-A3B-Instruct-2507@0d7cf23991Text (mixture of experts)Apache-2.0permYesRolling out
  • Each repository carries kllm-manifest.json: the base and its revision, the key fingerprint and key version, the kLLM version and the SHA-256 digest of every file.

Check a published model against its public base

With the model key, kllm verify seals the public base again under the same key and transform in a temporary directory and compares the result tensor by tensor, unlocked, together with the tokenizer, every other file and the manifest.

Verify Qwen3-30B-A3B-Instruct-2507-kllm
$ hf download ANetResearch/Qwen3-30B-A3B-Instruct-2507-kllm --local-dir Qwen3-30B-A3B-Instruct-2507-kllm
$ kllm verify Qwen3-30B-A3B-Instruct-2507-kllm --base Qwen/Qwen3-30B-A3B-Instruct-2507 --revision 0d7cf23991f47feeb3a57ecb4c9cee8ea4a17bfe --key Qwen3-30B-A3B-Instruct-2507-kllm.key
Under perm, every tensor must be equal to the bit; tensors that sealing rewrites in floating point may differ by one unit in the last place.

Apply for model keysThe catalog in the documentation

Library tags

Tags

Each tag pins an upstream Hugging Face revision, so the same alias downloads the same bytes on every host. Sizes and minimum GPU memory are taken from the kLLM library.

Library tags for Qwen3-MoE
AliasUpstreamSizeMin. GPU memoryTransformsCopy
qwen3-moe:30b-a3bdefaultQwen/Qwen3-30B-A3B61 GB71 GBorth, perm

Security

What sealing does not hide for this model

  • To an operator who reads GPU memory, user text reduces to a substitution cipher over tokens: 23.8% of tokens were decoded by unigram frequency alone on Qwen3-8B.
  • The sealed weights derive from a public checkpoint. Locked files cannot be matched to it without the key; once unlocked to run, a holder of that checkpoint recovers the transforms and 98.5% of the vocabulary permutation (measured on Qwen3-8B), for unmodified public models and fine-tunes alike.
  • Client-keyed mode keeps the key and the tokenizer on the user's machine. The copy that the cloud serves is unlocked on the user's side (kllm cloud --key), because the cloud computes with the weights. The cloud server (kllm serve --client) binds 0.0.0.0 by default and has no authentication; bind it to 127.0.0.1 or place it behind a gateway.
  • Unless sealed with --no-lock, the weights on disk are locked: encrypted in place with AES-256-CTR under a key derived from the model key. kllm serve --key unlocks them into ~/.cache/kllm/unlocked (KLLM_UNLOCKED_DIR, mode 0700) before the engine starts, so whoever runs the model holds the unlocked weights. Each tensor's largest binary exponent is stored in the clear.
  • Every approved holder of an official model's key holds the same model key, and the team derives it from an offline master key. A model key is not a confidentiality boundary between its holders; for a key known to no one else, seal the model under a private key.
  • Outside a confidential VM, kllm serve --key decrypts the tokenizer into host /dev/shm and offers no protection from the host. It is a development mode.

Security modelMeasured limits in the documentation