Spec: record sharing the local inference host as an open decision

nisse-dev and nisse-ops both send local roles to the one 24GB host,
where llama-swap keeps one model resident. Different models per agent
would reload weights on every switch. Per-key rate limits don't help.
Lists the options, simplest first, to decide after the executor
benchmark and the ops evaluation.

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

docs/Architecture-Build-Spec.md

@@ -1414,6 +1414,24 @@ The rest resolve when reached, not now.
   and ticket-driven ops work already require one. Whether every gated work item
   needs one (for instance, an ops question that turns into a change to make)
   is best settled once C2's step sequences are in use.
+- **Open: sharing the local inference host between agents.** Each agent keeps
+  one active work item (0.2), but `nisse-dev` and `nisse-ops` are separate agents,
+  and both send their local roles through LiteLLM to the one 24GB host. llama-swap
+  there keeps one model resident at a time. If the two agents' local roles map to
+  different models, interleaved requests reload weights on every switch (10–60 s
+  each), and each agent waits on the other's loads. LiteLLM's per-key rate limits
+  don't address this: they cap one key's throughput rather than share the host,
+  and know nothing about which model is loaded. (Measured 2026-09-28: they only
+  throttled the benchmark, and the token limiter leaked until LiteLLM restarted.)
+  Options, simplest first:
+  - *One local model for every local role.* No swaps at all. It needs one model
+    good enough both as the dev executor and for ops' Home Assistant tool calls.
+  - *Two models co-resident*, if VRAM allows (llama-swap groups): for instance a
+    small tool-caller for ops beside the executor model. It costs context window.
+  - *A scheduler in front of the host* that batches each model's requests to keep
+    swaps rare. The most work; only if neither of the above fits.
+  Decide once the executor benchmark and the ops evaluation show whether one model
+  can serve both roles.
 
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