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>
