An embedding model is served for embedding, with many slots

ctl model serve served one as a chat model: no --embedding, four slots, a
512-token batch, embeddings unset, and a chat smoke test it could not
pass. A GGUF that pools (<arch>.pooling_type), or --embeddings, gets
--embedding, 32 slots of 1024 tokens unless sized otherwise, a batch that
holds a whole passage, the embeddings capability, and one vector asked for
as its smoke test (canaries and updates too). An import of a hundred
thousand chunks had gone at the pace of four slots with the card a third
busy.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>

internal/cli/model_serve.go

@@ -60,6 +60,8 @@ func newModelServeCmd(client func() *ctlClient) *cobra.Command {
 	f.StringVar(&req.Quant, "quant", "", "the quantisation to serve (default: the one that suits the cluster best)")
 	f.StringVar(&req.File, "file", "", "the file to serve, instead of choosing by --quant")
 	f.StringVar(&req.Projector, "projector", "", "a vision model's mmproj file, fetched and loaded beside it")
+	f.BoolVar(&req.Embeddings, "embeddings", false, "serve it as an embedding model (its GGUF header says so for most): "+
+		"--embedding, 32 slots of 1024 tokens unless --ctx-pool or --context-length says otherwise")
 	f.StringVar(&req.Format, "format", "gguf", "gguf, run by llama.cpp, or safetensors: the original weights, run by vLLM on GPUs")
 	f.BoolVar(&req.CPU, "cpu", false, "run it on the CPU, whatever the plan says")
 	f.StringVar(&req.GPUClass, "gpu-class", "", "run it on GPUs of this class, whatever the plan says")