A big model's download no longer runs out its startup timeout

The startup timeout runs from when a node's download last moved, so a
model of hundreds of gigabytes on a slow mirror is not failed while it
arrives. ctl model serve --env sets engine variables (NCCL_DEBUG=INFO)
on every node, and a layout given by hand keeps expert parallelism for a
model of experts. docs/large-models.md: how a model is split, what each
way needs of a card, and a checklist for bringing one up on a fabric.

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

docs/admin-api.openapi.json

@@ -10639,6 +10639,10 @@
             "format": "int64",
             "description": "KVBytesPerToken is one token of context, the whole model: what an option needs for another context is worked out from it. 0 is unknown."
           },
+          "experts": {
+            "type": "integer",
+            "description": "Experts is a mixture-of-experts model's routed experts; 0 for dense."
+          },
           "gated": {
             "type": "boolean"
           },
@@ -10789,6 +10793,13 @@
             "type": "integer",
             "description": "CPUCores is what a replica computes on; zero takes half the smallest node it fits on."
           },
+          "env": {
+            "type": "object",
+            "additionalProperties": {
+              "type": "string"
+            },
+            "description": "Env is set in the engine's environment, on every node of a replica: NCCL_DEBUG=INFO to see how its cards talk, NCCL_IB_* to steer them. Only the variables engines are tuned with; the catalog refuses others."
+          },
           "memory_gb": {
             "type": "integer",
             "description": "MemoryGB is what a replica on CPUs reserves, in place of the figure worked out from its weights and context: a judge's, whose own sizing is known."