feat(llm-inference): move astro-orbiter monitoring to GitOps (values.yaml + dashboards.yaml)

- Prometheus scrape configs for node/gpu/llama-server exporters on
  astro-orbiter now declared in cluster/applications/monitoring/values.yaml
  (additionalScrapeConfigs), applied via ArgoCD sync instead of an
  imperative kubectl secret patch from the Ansible role.
- Grafana dashboard for astro-orbiter LLM inference added as a ConfigMap
  in cluster/applications/monitoring/dashboards.yaml (grafana_dashboard=1
  sidecar label), replacing the role's ad-hoc kubectl apply of a rendered
  Jinja template.
- ansible/roles/llm-inference/tasks/monitoring.yml: removed the kubectl
  get/patch/apply tasks and orphaned grafana-llm-dashboard.json.j2
  template; role now only stands up node_exporter + nvidia_gpu_exporter
  and verifies they're reachable — cluster-facing config lives in Git.
- host_vars/vars.yml + inventory.yml: finalize astro-orbiter as the
  llama.cpp/RTX 3090 host (jarvis user, ssh key), drop stale Ollama/AMD
  vars and ollama_server inventory group superseded by the ATX rebuild.

ansible/roles/llm-inference/tasks/serve.yml

@@ -22,6 +22,17 @@
     state: present
     update_cache: false
 
+# NOTE: nvidia-driver-595-open provides the runtime driver only (nvidia-smi,
+# libcuda.so) — it does NOT ship nvcc/CUDA headers needed to build GGML_CUDA=ON.
+# Ubuntu 24.04's nvidia-cuda-toolkit (12.0.x) is sufficient to build llama.cpp
+# against; it does not need to match the 595 driver's CUDA 13.2 runtime version.
+- name: Install NVIDIA CUDA toolkit (nvcc) for building llama.cpp with CUDA support
+  ansible.builtin.apt:
+    name: nvidia-cuda-toolkit
+    state: present
+    update_cache: false
+  become: true
+
 - name: Clone llama.cpp repository
   ansible.builtin.git:
     repo: https://github.com/ggml-org/llama.cpp.git
@@ -30,6 +41,20 @@
     update: false
   become: true
 
+- name: Check for incomplete/stale llama.cpp CMake configuration
+  ansible.builtin.stat:
+    path: /opt/llama.cpp/build/Makefile
+  register: llama_cmake_generated
+
+- name: Remove stale llama.cpp build dir if CMake configure never completed
+  ansible.builtin.file:
+    path: /opt/llama.cpp/build
+    state: absent
+  become: true
+  when:
+    - not llama_cmake_generated.stat.exists
+    - not (ansible_check_mode | default(false))
+
 - name: Build llama.cpp with CUDA support
   ansible.builtin.command:
     cmd: cmake -B build -DGGML_CUDA=ON -DCMAKE_BUILD_TYPE=Release
@@ -53,6 +78,11 @@
     group: "{{ llm_venv_owner }}"
     mode: "0755"
 
+- name: Check whether GGUF already exists (avoid re-downloading 16.6GB on every run)
+  ansible.builtin.stat:
+    path: "{{ llm_gguf_path }}"
+  register: llm_gguf_stat
+
 - name: Download Gemma 2 27B Q4_K_M GGUF from HuggingFace
   ansible.builtin.get_url:
     url: "https://huggingface.co/bartowski/gemma-2-27b-it-GGUF/resolve/main/gemma-2-27b-it-Q4_K_M.gguf"
@@ -63,8 +93,13 @@
     group: "{{ llm_venv_owner }}"
     mode: "0644"
     timeout: 7200
+    force: false
   become: true
   no_log: true
+  # Idempotency: skip entirely once the file exists and is reasonably sized
+  # (the finished GGUF is ~16.6GB; guard against a truncated partial download
+  # being mistaken for complete by only trusting files > 15GB).
+  when: not llm_gguf_stat.stat.exists or (llm_gguf_stat.stat.size | int) < 15000000000
 
 - name: Disable and stop vllm-serve if present
   ansible.builtin.systemd: