FreeToken (FlashML-org/FreeToken) serves mixture-of-experts models several
times a card's size from one card, experts in RAM, from Hugging Face
safetensors checkpoints. Installed like vLLM, from PyPI by uv, on every node
it runs on: compute capability 8.0+, driver 580+, x86-64, and the CUDA 13
toolkit it compiles kernels with. Older cards are passed over: on cuda01's
Quadro RTX 6000 (7.5) 0.2.1 had no kernel image for the first prefill, and
in fp16 hit an illegal memory access on its first request.
ft serve gets the model, address, card and served name; the key goes in
FREETOKEN_API_KEY. Flags that read files or fetch are refused, as are
FREETOKEN_ variables naming paths or the agent's own settings. ctl model
context sets --max-seq-len-override.
Readiness now also waits for an engine whose /health answers 200 while its
body says "loading", as FreeToken's does from the moment its HTTP server is up.
FreeToken is followed release by release: a weekly workflow fails when PyPI
has a version newer than DefaultEngines installs (scripts/engine_releases.py).
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Everything in server.yaml that is not one machine's own is now a versioned
document in the store (cluster_settings), the same on every control plane.
The first start against a store imports the file's cluster keys, with the
baseline guard policy when there is none; after that those keys in a file
are ignored, with a warning.
Each control plane polls the document's version and its own server.yaml's
hash. A change is validated and applied: guard live (Gate.Replace), anything
else by a graceful restart that systemd brings back, standbys first and the
leader 30s later. A version that does not load on a machine is not applied;
at start the newest one that loads is used, so a bad edit cannot keep every
control plane down.
llmgw ctl config show/get/set/unset/edit/history/rollback, the admin API
under /api/v1/settings, and the console's Settings screen, which layers the
store's document over the machine's file, write it. Commands beside the
control plane that read cluster keys (init, node add, server add, backup,
upgrade) read the layered configuration; init --bundle writes offline to
the store once it holds the settings.
A standby that becomes the leader re-reads models, engine choices and
facility holds from the store.
Retires appendGuardIfMissing: the import does it.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A node that cannot run the CUDA 13 build installs the same version's
CUDA 12.9 build from vLLM's GitHub release, with PyTorch's cu129 torch;
the install computes on the GPU before it counts. The build can be
chosen per node — auto, cu13, cu129, none — on the cluster page, with
ctl engine-build, or over the admin API; the control plane keeps the
choice and tells the node at once.
Refs #2.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
