Reading the tool's own output against the real fleet: it chose q8_0 as the
best fit and emitted a vLLM profile with no quantisation. q8_0 is a GGUF
quantisation that only llama.cpp serves, and from a file rather than a
repository — so following that entry loads full-precision weights, which
the same output had just listed as not fitting one card. The planner would
have caused the out-of-memory it exists to prevent, from a recommendation
that looked right.
Choosing a precision and choosing an engine turn out to be one decision, so
a Precision now carries the engine that serves it, the quantisation flag
where one is needed, and whether it lives in a file. A GGUF fit prints a
path and says to download it; a vLLM fit prints the repository and passes
--quantization so the engine does not quietly load bf16.
The arithmetic was right and the recommendation was wrong, which is the
failure mode worth having tests for: everything reported a number, and the
numbers agreed with each other.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Running the planner against the real fleet produced twenty-six rows for a
32B, of which thirteen were duplicates: two fabrics of identical cards, and
every single-node fit listed once for each.
A replica on one node does not span anything, so which fabric its node
happens to be attached to changes nothing about whether the model fits. A
multi-node fit is still listed per fabric, because there the fabric is the
constraint and which one has room is the thing the operator needs told.
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>
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>
