infra/llama-cpp: add stack — llama-swap fronting Qwen3-VL + MiniCPM-V

Single Deployment of mostlygeek/llama-swap:cuda hot-swaps three
GGUF vision models (qwen3vl-8b, minicpm-v-4-5, qwen3vl-4b) at one
OpenAI-compat /v1 endpoint on Service llama-swap.llama-cpp.svc.
Idle TTL 10min so models unload between benchmark batches.

Storage: NFS-RWX from /srv/nfs-ssd/llamacpp (30Gi). One-shot
download Job pulls Q4_K_M GGUF + mmproj per model, creates stable
model.gguf / mmproj.gguf symlinks so the llama-swap config is
filename-agnostic, then warms the kernel page cache.

GPU: nvidia.com/gpu=1 = whole T4 — operator must scale immich-ml
to 0 during benchmark windows. wait_for_rollout=false so apply
doesn't block on GPU availability.

Initial use case: vision-LLM benchmark for instagram-poster
candidate scoring; future consumers (HA, agentic tooling) hit
the same endpoint via LiteLLM at the gateway.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

stacks/llama-cpp/terragrunt.hcl

@@ -0,0 +1,23 @@
+include "root" {
+  path = find_in_parent_folders()
+}
+
+dependency "platform" {
+  config_path  = "../platform"
+  skip_outputs = true
+}
+
+dependency "vault" {
+  config_path  = "../vault"
+  skip_outputs = true
+}
+
+# llama-cpp: in-cluster vision-LLM server. One Deployment of
+# `mostlygeek/llama-swap:cuda` fronts three models (qwen3vl-8b,
+# minicpm-v-4-5, qwen3vl-4b) at a single OpenAI-compat /v1 endpoint
+# on Service `llama-swap`. llama-swap loads/unloads per-model
+# llama-server subprocesses on demand (idle TTL 10 min). The T4 is
+# allocated wholly to this pod; immich-ml must be scaled to 0 during
+# benchmark runs. See infra/docs/architecture/llama-cpp.md for the
+# full rationale (build ≥ b6907 for Qwen3-VL, T4 FP16/INT4 only,
+# llama-swap over Ollama, etc.).