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withdrawn.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 catalog model with external {url, model, api_key_file} and no profiles
is published as an endpoint by the control plane, the key read from its
file each time; the router rewrites the request's model to the provider's
name and sends its key. It is never scraped nor stale, and nothing is
placed for it. Named in fallbacks, it serves when the cluster has no room.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Engines listen on every address their node has, with no key. Checked on the
live cluster: from a neighbouring node, a request straight to an engine's
port was answered — past the gateway's authentication, quotas and
accounting, by anyone who could reach the node.
Each replica's engine is now started with a key and refuses requests without
it. The key is derived from the replica id with an HMAC over a secret kept
in the store, rather than stored per replica: the router has to send the
right key to replicas this process never started — adopted after a restart,
or placed by the other control plane — and a function of a shared secret
gives that without a table to keep in step. A key taken from one node opens
that replica only. The secret is made on first use and copied with the rest
of the store when moving to PostgreSQL; a new one would lock the router out
of every running engine at the moment of the move.
The key reaches llama.cpp and vLLM through their environment
(LLAMA_API_KEY, VLLM_API_KEY) rather than their command line, which every
user on the node can read; SGLang only takes it as an argument. The agent's
readiness check, the router's proxy and its metrics scrape send it, the
client's own Authorization still never reaches the engine, and `ctl bench`
fetches the key of the replica it is pointed at from the admin API.
Engines started before this keep running without a key until their replica
is next started. With no store there is nowhere to keep the secret, and
engines start without one, with a warning.
Also formats two files that went in unformatted.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Two things a cluster with many nodes and many GPUs needs that were missing,
and they are the same thing seen from two distances.
Idle tracking. The control plane knew what was placed and whether it was
healthy, never whether anyone wanted it — so on a fleet with more models
than cards there was no way to answer the first question anyone asks, which
is what can I stop. The router is the only thing that knows, so it now
reports it: a timestamp written in Acquire, where a request already meets an
endpoint under a lock already held, and collected once a tick as a map. That
shape is the point. Per-request notification would be a firehose carrying
one bit — the newest timestamp wins — and this costs the same on four
replicas as on four hundred.
It travels through an interface in the direction opposite EndpointSink, for
the same reason that one exists: the control plane decides what should run,
the router knows whether anyone wants it, and neither should need to be in
the other's process to say so.
Unknown is kept distinct from idle throughout. A router that has just
restarted knows nothing, and a zero timestamp would be the oldest idle
replica in every comparison — so one restart would nominate an entire fleet
for eviction. Absent from the map, "-" in the table, negative in the JSON,
and IdleFor returns a second value rather than a duration anything could
act on by mistake.
ctl status. A per-node listing is right for four nodes and wrong for two
hundred: how full am I, what is broken, what is holding cards nobody is
using are aggregates, and reading them off a long table is a chore, which
means it gets skipped. Capacity per GPU class with usage bars, free whole
nodes per fabric — the number that decides whether a multi-node replica can
land, since free cards on the wrong fabric are not capacity for a model that
spans nodes — replica and model counts, models that cannot be placed, and
what has gone unasked-for. Derived from the same state the tables show, so
it cannot disagree with them.
Unhealthy GPUs count as neither used nor free, because counting them free is
how a cluster looks like it has room it does not.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
llama.cpp is what makes a cluster with no cards useful: it serves the same
OpenAI API on CPU, so the whole path — placement, supervision, scraping,
scheduling, admission — can be exercised end to end on ordinary machines.
Three things had to give way for it.
A profile may now omit its GPU class, and that is how it says it runs on the
CPU. Only engines that can actually serve without a card may omit it; for
vLLM an omitted class stays an error, because otherwise a typo becomes a
replica placed on a machine with no hardware and diagnosed minutes later
during model loading. Placement spreads CPU replicas instead of bin packing
them, since unlike GPUs there is no allocation to stop two of them sharing a
machine's cores.
Metric mappings are now per engine rather than per cluster. llama.cpp calls
the same three numbers requests_processing, requests_deferred and
kv_cache_usage_ratio, and a single global mapping would leave half a mixed
fleet unmeasurable. An empty mapping still defaults to vLLM's names; one that
names its own Running metric is taken as a complete statement, so the fields
it leaves blank stay blank rather than being filled with names that engine
cannot have.
The adapter refuses multi-node and tensor-parallel profiles. llama.cpp splits
across local GPUs by a different mechanism than this adapter drives, and
accepting either would produce a replica that starts and then serves from one
machine while the catalog believes it has several.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Inserted between quota checks and scheduling: admission answers when a request
may proceed, the scheduler still answers where it goes. Keeping them apart is
the point — an engine queue is FIFO and knows nothing about tenants, so once a
batch job is in it, no endpoint selection rescues the request behind it.
- saturation detector: one number per pool, the worse of queue depth and KV
occupancy, since either alone misleads.
- priority bands with per-band saturation ceilings, so the cluster degrades from
the bottom up.
- tenant round-robin within a band: one caller sending a thousand requests takes
one slot in the rotation, not a thousand.
- bounded queues with capacity (503) and TTL (504); cancelled requests leave the
queue instead of holding capacity.
- metrics for queue depth, wait time, rejections and pool saturation.
Exit criterion, measured in test/e2e: with 500 concurrent bronze requests
running continuously, interactive p99 in the platinum band stays at 1.04-1.24x
idle against a 1.5x budget. With admission off the same load pushes it to
55-60x. Both directions are asserted, so the test fails if admission regresses
and also if the load generator stops being a real load.
Two lags had to be closed, and they needed different answers. Engine metrics are
scraped and lag an interval; router inflight does not, but it counts running
requests, and treating those as queue makes a healthy engine look overloaded
every interval. What is actually queued is inflight minus batch capacity, and
capacity is not in the metric contract, so it is learned as the high-water mark
of observed running. Separately, between admission granting a request and the
router dispatching it the request is invisible everywhere, so a simultaneous
burst passed the check en masse; the controller now counts its own outstanding
leases against dispatched inflight.
One plan correction: a saturation ceiling of 1.0 was documented as "never held
back". It is not — it means "flow up to the healthy limit". A band that must
never wait needs a ceiling above 1. Plan §7.2.2 updated.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The code behind plan §7.3-7.6:
- breaker: folds cooldown and circuit breaking into one component
(FailureThreshold=1 gives LiteLLM-style cooldown), with exponential backoff
and half-open probes.
- endpoint: live state registry. Inflight counting, staleness detection, drain
rate derived from counters. A scrape failure does not trip the breaker — the
engine may be serving fine while only its metrics endpoint is down.
- scrape: parses the §7.6 metric contract. Errors when any of the mandatory
three is missing; the metric name mapping is overridable, since names drift
between versions (plan §14).
- scheduler: the filter -> score -> pick pipeline. A "strategy" is now just a
set of scorer weights; chat, batch and long-context are the same code with
different weights. Scorers: queue, kv (hard penalty above the ceiling),
inflight, load, weight, session (bounded-load consistent hashing). Pickers:
max-score, p2c, softmax, round-robin.
Decision carries the rationale — which scorer produced what, which filter
dropped how many. Tuning the weights without that is guesswork.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
ilkerandClaude Opus 5