A profile's replicas, set on any of a model's profiles, is how many of its
replicas run there; the scaling becomes the sum. The reconciler starts the
ones short of their profile before stopping the ones over, so a change of
split never runs fewer. ctl model replicas sets them, adding a profile on
CPUs to a GGUF model; the router already fills the higher-priority pool
first, which the CPU profile is put below.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
max_output on POST /api/v1/models/{name}/context, --max-output on ctl model
context, and a field in the console's Context length… form. Alone it changes
only the catalog's limit, which the router holds max_tokens to, and starts no
replica again; with a context it is set with it, never past it.
Strata's context is now changed like the others', through its setup's
--context, which writes the engine's start script at each start. Neither
needs a model removed and added again any more.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
llmgw ctl model gpus MODEL N [--profile P] [--in-place],
POST /api/v1/models/{name}/gpus and the console's GPUs…: how many cards of
one node each replica uses. Tensor parallel for vLLM, SGLang and
TensorRT-LLM, held to the model's KV heads (modelplan.SplitsHeads, now one
rule for the planner and this); the layers split for llama.cpp, its old
--tensor-split dropped. Refused for a count no node has, for cards that
would not hold the weights and the context, and for engines on one card.
The replicas start again beside the old ones; with no room beside them the
command says so, and --in-place stops each first.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
ctl model context MODEL TOKENS [--slots N], POST /api/v1/models/{name}/context
and the console's Context length…: the catalog, the engine's arguments and
the KV memory change together, refused past the trained context or what the
cards hold, then the replicas restart one by one.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A site whose catalog had no flux-schnell or triposr showed no Images or 3D
page, and nothing said they were one click away. The cluster page now has
a Pictures and 3D models card under the models: each preset, what it
makes, the classes of card here it fits, whether and by whom it was
added, and why the leader has not added it by itself; an Add button, which
says first when it would install ComfyUI on the GPU nodes and restart the
control planes; and the switch for presets.auto. The console's models
screen shows the same under its table: A adds one, asking first in the
same case, and P turns automatic adding on or off.
`ctl model add PRESET` and `ctl model presets` go through the presets API,
so the control plane keeps who added one, and `ctl model presets auto
on|off` flips the setting; against a control plane from before, add sends
the entry as v0.14.4 did.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
flux-schnell and triposr come with llmgw as presets: their workflow,
custom nodes, packages and licence as they were found and tried on the
PoC, and a profile that says the card it needs rather than a class, so the
entry places on any site's cards. TripoSR's workflow is built in, with the
LLMGWPrepareForMesh node that keeps a picture's background out of the
mesh, which the copy in the document had left out.
`ctl model add PRESET` adds one, and has the nodes install ComfyUI when
the cluster's settings name none; `ctl model presets` lists them; the
console's add-model dialog offers them beside a definition file. A
ComfyUI build is now also checked to keep its order on a Turing card, so
a node's installed cu13 is not replaced by the cu126 build.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
The catalog had one source a model, so an embedding model served by vLLM
from its repository on a card and by llama.cpp from a GGUF on CPUs had its
entry swapped by hand halfway through the metallurgy import. A profile's
source is what its engine loads and placement looks for; the model's is
the default, and what updates and canaries move. ctl model add checks
every source, the GGUF check reads each llama.cpp profile's, and ctl
weights collects none a profile loads. An empty source leaves a profile's
fingerprint as it was, so running replicas do not read as stale.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Conflicts with facility-aware placement (config, control, admin API routes,
store test tables) all keep both; the generated reference and OpenAPI
document regenerated.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
ctl federation add/list/remove/sync keeps peers in the store (migration
0029); their /v1/models are offered here as external models named
PEER/MODEL and kept in step, a model still named by a fallback kept
withdrawn. Peers are checked at /ready, timed, and taken out of routing
while down. Requests to a peer carry x-llmgw-hop and are refused (508) when
they come back to a cluster they passed through; residency and no_store
keep requests off peers; X-Request-Id and, with --send-tags, attribution
tags go along. ctl model fallbacks sets overflow to a peer. Console view,
metrics, admin API routes and docs.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A vLLM model can carry PEFT adapters, registered at start, or with
max_loaded_adapters held at most N per replica: the router loads an adapter
through vLLM's runtime LoRA API when a request first wants it, sends the
adapter's requests to a replica that holds it while that one is not
saturated, loads each cold adapter once, and unloads the least recently used
idle one to make room. llama.cpp keeps loading every adapter at start.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A suite is a set of cases in YAML or JSON lines, each scored by exact,
contains, regex, a number within a tolerance, a JSON Schema subset, or a
judge model with a rubric; weighed into one score against a threshold.
internal/bench/eval runs one on the control plane through Router.Replay, as
the gateway's own requests, and keeps every run and its cases in the store
(migration 0028: eval_runs, eval_cases).
- llmgw ctl eval MODEL --suite FILE|NAME, --history, --run, --suites; exits
non-zero below the score. Suites by name from control.eval_dir, or the
two built in from deploy/eval (arithmetic, json-format).
- model update and promote take --eval SUITE --min-score N, or a model's
standing gate (ctl model gate, eval: in the catalog): a promote asks the
canary first and leaves it below the score; an update tries the new
weights as a canary given no share, then promotes or removes it.
- The console's model menu: ask a suite with its progress, its runs, its
gate.
- The audit middleware no longer hands a handler a body cut off at 64 KiB.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
The mirror keeps where it fetched a file beside it, and reports that and the
checksum after it restarts. A model whose files the mirror cannot trace is
found on the registry: a repository holding a file of that name that has the
commit the mirror's directory is named after, with every file at the same
size, the most downloaded first. ctl model origin and the console set one by
hand, checked the same way. Commits compare shortened.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A model served from the registry records its origin: repository, commit and
each file's checksum; one served before is worked out from the mirror's
record of where it fetched the file. Every mirror.update_check (6h) the
control plane asks whether those files changed there. ctl model updates
lists it, ctl model update fetches beside the old files, checks, swaps and
replaces replicas one at a time, ctl model rollback goes back. The console
shows it on Models and offers the same.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
llmgw ctl model adapters lists a model's adapters and adds or removes
them through POST /api/v1/models/{name}/adapters, which reads each new
file first and refuses one that is not an adapter for the model's
architecture, as the mirror page's card does.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Stored ctl bench results reach the reconciler: requests per replica past
the measured SLO concurrency scale a model up, a replica is taken back only
if the rest stay under 70% of theirs, and among profiles of equal priority
the most measured tokens per GPU or core is tried first. ctl model target
sizes a model for a throughput against the room there is.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A model has an owner (org, team or project). Quotas guarantee a scope
GPUs, cores and memory and may limit it; what a scope holds is summed from
its models' replicas and those beneath it. A replica within its owner's
guarantee is not taken for another scope; what is held beyond a guarantee
is borrowed and taken back for a scope within its own. A limit stops a
model before it starts, and headroom stops there too. ctl quota and ctl
model owner set them; metrics and a CapacityAtLimit alert report them.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A model's class (critical, standard, opportunistic; unset follows its
scaling) decides what may be stopped to make room for it: a lower class
for a higher, and within a class only what another grew into under load,
for a model below its minimum. Victims are tried on a copy of the
inventory and stopped only if they make the room. Preempted replicas are
drained with a short grace and their model is held back until the room is
used. ctl model resources and class move under ctl model.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A profile's cpu_cores is reserved out of its node's CPUs by placement, as
memory is, and becomes the engine's thread count. Agents report their CPU
count in registration and heartbeats. New CPU models must declare memory;
model views show reserved against measured memory with warnings, and
ctl model resources sets both for a stored model.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
CPU replicas start on half the machine's CPUs, not all of them: two busy
llama.cpp servers each spinning on all 8 vCPUs fell to 0.8 tok/s apiece,
where at 4 threads they keep 5.5. llamacpp.threads sets it per node.
Inference requests can be traced over OTLP/HTTP (tracing.endpoint), with
admission, schedule and upstream spans and traceparent sent to the engine.
Security: onboarding no longer puts the cluster root in the system trust
store and removes it where it was; the mirror's mutual-TLS API can be
limited to named certificates (--api-client); init.log no longer keeps the
administrator's password; a warning when auth is off on a public listen;
the recorded actor is sanitised and sudo-aware; Ray's dashboard is off, and
the remaining engine gaps are documented.
reconciler.go and agent.go are split by topic, the status mapping of each
failure is tested, and the plan's checklist is brought up to date.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Adding a model took four steps and a YAML file — plan it, fetch it into the
mirror, write its catalog entry with a memory figure somebody estimated,
scale it — and whether it would load was found out at the end.
`llmgw ctl model serve OWNER/REPO` does it as one job in the control plane,
where the registry, the mirror, the catalog and the fleet already are:
- the registry: the GGUF file set matching --quant (Q4_K_M by default) or
--file, leaving out vision projectors and importance matrices, and naming
what the repository does have when nothing matches; gated repositories are
refused;
- the mirror: fetched at the repository's revision, with progress, and each
file's sha256 compared with what the registry published — the mirror is
the one place a corrupted download is caught once rather than loaded on
every node;
- the check: whether the nodes' llama.cpp loads the architecture, and the
memory it needs from its own header;
- the catalog, then a wait for a replica that stops early, with the reason,
when the model is parked or fits on no node;
- a smoke test: one short request to the replica, with its engine key, and
the answer and tokens per second.
ctl starts the job and prints each step as it changes; a download of hours
does not depend on the terminal. --on-demand adds the model without starting
a replica. Jobs are kept in memory until the control plane restarts.
The catalog name a repository suggests moved into the registry package, so
the mirror page and this agree on it.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Two defects, both of the same kind: something the CLI knew and the rest of
the system did not.
A profile can be edited while a replica is running against the old one. The
reconciler deliberately leaves it alone — churning a replica that is serving
costs more than the imperfection — but there was no way to see it, so the
cluster could disagree with its own configuration indefinitely and read as
healthy. Profile now carries a fingerprint, a replica records the one it was
placed with, and both the API and `ctl replicas` say "stale profile" when
they differ. An empty fingerprint is not stale: it means a replica adopted
from an older agent, which is unknown rather than wrong.
Adding a model from the admin page skipped the source check `ctl model add`
has done all along, so a typo or a mirror that is down became a replica
failing on a node minutes later, with an error naming the node. The check
moves out of the CLI into a package both use.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The count is scaling.min, and until now the only way to change it was to edit
server.yaml on the host and restart — or, for a model added at runtime, to
remove it and add it back, which means having its whole definition to hand.
`llmgw ctl scale <model> --min N` instead. Only the scaling block moves:
everything else is read back from what was stored and written out untouched, so
changing a number cannot quietly become a redefinition.
The catalog is rebuilt and validated before anything is written, as in Add. A
max below a min is refused with the stored definition untouched — persisting a
rejected change would come back on the next restart as a catalog that does not
build.
The answer reports the scaling in force rather than the request. The catalog
fills in a max that was left out, and a caller echoing its own request would
print a number the cluster is not using.
A model declared in configuration is refused, for the reason Remove already
refuses one: the YAML is read on every start, so the new count would hold until
the next restart and then silently revert. A cluster disagreeing with its own
config file is worse than an error saying where to edit.
The help says what --max does, because it is not what it looks like: a model
with a minimum runs exactly that many and nothing scales it up under load, and
an on-demand model loads one replica however high its max.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The mirror was a directory and a discipline written down in a README. This
makes the three operations that matter into commands, on the mirror host,
where `llmgw keys` already sets the precedent of operator tooling that works
on files rather than through the API.
The gateway still never writes to the mirror. That property was chosen
deliberately — a mirror writable by the thing reading from it is a way to
replace a model with one every node then fetches without complaint — and a
person running a command does not take it away. `serve` states it rather than
inheriting it, so a handler added later cannot quietly make the mirror
writable.
`add` downloads to a temporary name and renames, for the reason the agent's
fetcher does: a mirror is copied to every node, so a partial file here is a
partial file everywhere. It verifies the length the server promised, reports a
checksum — the mirror is the one place a corrupt file reaches every node
rather than being caught once — and refuses to replace an existing version,
which is the whole point of the layout.
`unused` refuses to answer when it cannot read the catalog. An empty catalog
and an unreachable one produce the same list, everything, and the obvious next
step after that list is deletion. It compares by path rather than by spelling,
because a base typed with a different scheme or a trailing slash would
otherwise report a file in use as unused.
`remove` checks the catalog first and says to retire the entry before deleting
the file, because a node that has to refetch a deleted file cannot, and that
failure arrives later, on the node, looking like something else.
Verified end to end: Range requests answer 206, which is what makes an
interrupted fetch on a node resume; writes answer 405; a traversal attempt
answers 404.
Note: internal/agent/supervisor's TestSidecarRunsBeforeEngine fails under full
suite load on main as well as here — it asserts that one shell's echo lands
before another's, which the supervisor does not promise. Everything else
passes. Left alone rather than fixed in a commit about mirrors.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
llmgw ctl model plan Qwen/Qwen2.5-Coder-32B-Instruct
The arithmetic this replaces is the last thing between having GPUs and
serving a model on them. Choosing tp, gpu_class and a context that will
actually fit means knowing the layer count, the KV head count, what the
weights weigh at a chosen quantisation and how much of a card the runtime
keeps — and getting it wrong is not an error message, it is an
out-of-memory twenty minutes into a load. I computed those by hand for
every model on this cluster.
Both halves are read rather than retyped: the architecture from the model's
own config.json and parameter count, the hardware from the running fleet.
Attention heads in place of KV heads is the classic slip and is worth a
factor of five on a model with grouped-query attention, so it is not a
number anyone should be entering.
Every candidate is listed, not one answer. With cards to spare you want the
full-precision fit; with one card free you want to know a quantised one
exists. Choosing between those is not the planner's job.
The tests are calibrated against files actually downloaded here — llama.cpp
reported 14.62 GiB and 8.37 GiB for the 14B at q8_0 and q4_K_M, and the
model reproduces both — and they caught two errors in my own arithmetic.
The overhead was charged per rank from the whole model's weights rather
than each rank's share, and it reserved a tenth of a card that
usableFraction had already reserved. Between them, Llama-3.3-70B at bf16
across two 80 GB cards came out as not fitting, which is a configuration
people run every day. Slack reserved in two places, each unaware of the
other, reads as caution and behaves as a bug.
Pools are grouped by class and fabric together, because a replica may never
span two fabrics: eight free cards split across two of them are not eight
cards for a model that needs eight, and merging them would plan something
that starts, passes its health checks and hangs on its first all-reduce.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The last piece of not restarting a control plane to try a model:
llmgw ctl model add -f qwen-coder-32b.yaml
llmgw ctl model remove qwen-coder-32b
The file is the same YAML a model takes in server.yaml, read by the same
koanf parser through the same tags, so an entry moves between the two by
being copied and a field that works in one cannot silently mean nothing in
the other.
Wired only when there is a store. A control plane without one says so
rather than accepting a model that vanishes on the next restart, which is
the kind of success that wastes an afternoon. At startup the catalog is
rebuilt from configuration plus what was stored, so models added before a
restart are still there after it — without that the whole feature would be
a slower way to lose work.
Every failure adding a model is the definition being wrong — a collision, a
missing fallback, a profile that cannot be placed — so they are 400s. A 500
would send someone to the server's logs for a mistake in their own file.
The actor comes from the caller and is kept with the row, so a catalog that
has grown over months can say where each entry came from.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
