Placement had no idea how much memory a node has or a model needs. A CPU
profile has no card to be free, so eligibleNodes accepted every ready node:
Qwen2.5-Coder 14B, whose working set is about 24 GB, would have been placed
just as happily on one of the 8 GB nodes, which reports itself perfectly
healthy right up until the OOM killer arrives.
node_selector was yesterday's stopgap and it is the operator restating by
hand something the system can know. Agents now report MemTotal, profiles
declare min_memory_gb, and placement filters on it, naming what was short —
"has 8 GB of memory, needs 24" is actionable where "no candidates were
considered" is not. The label selector stays, because "this node is for
coding models" is a real thing to want to say that memory does not express.
Two deliberate non-exclusions. A profile with no requirement fits anywhere,
which is every profile that exists today. And a node that reports no memory
is still usable: an agent older than the field says nothing, and letting a
control plane upgrade quietly empty half a fleet is a worse failure than the
one this prevents.
ctl nodes grows a MEM column showing "-" rather than 0 for a node that did
not say, because an old agent is not a small machine.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Agents have reported labels since the beginning and nothing read them. That
was survivable while every CPU profile was a 0.5B model that fits anywhere,
and stops being survivable with a 14B one: a CPU profile has no card to be
free, so eligibleNodes accepts every ready node, and placing Qwen2.5-Coder
14B on one of the 8 GB nodes would OOM a machine that reported itself
perfectly healthy.
Nothing in the inventory says how much memory a node has, and inferring it
would be guessing at the wrong layer — the amount that matters is the
model's working set, which the scheduler does not know either. So the
operator says which nodes a profile is for, with the labels that already
exist:
node_selector:
ram: 32g
A rejection names the label it did not find, because "no candidates were
considered" is not something anyone can act on and "does not carry ram=32g"
is. An absent selector constrains nothing, which is every profile that
exists today, and there is a test that says so.
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>
The two hard constraints from plan §1.1 are enforced structurally rather than
checked after the fact: there is no code path that can combine nodes from two
fabrics, and only GPUs of the profile's class are ever considered. Getting
either wrong produces a replica that starts, passes health checks, and then
hangs or crawls under load — which looks like success until traffic arrives.
- inventory: nodes, hardware, fabric attachments, allocations. A re-registering
agent cannot clear an operator's cordon, since a cordon is usually in place
because something is wrong with the node. A GPU with an ECC fault or another
process on it is not free in any useful sense. Replicas are released whole,
because they fail whole.
- placement: highest-priority profile that fits, with every rejected candidate
explained. "Unschedulable" on its own tells an operator nothing about which
of six constraints bit.
- bin packing fills partly used nodes first so whole nodes stay free for large
models that cannot be split; multi-node replicas get the emptiest machines
instead, being the least movable thing in the cluster.
- NVLink-aware GPU selection keeps a tensor-parallel group inside one domain
where the topology is known, falling back to lowest free indices otherwise.
Property-based tests generate 400 random clusters and assert the invariants on
every placement produced: one fabric per multi-node replica, only that fabric's
HCAs per shard, one GPU class, no unhealthy or already-allocated GPU. The
table-driven tests cover the cases someone thought of; these cover the rest.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
ilkerandClaude Opus 5