fix(llm-inference): switch serve phase from vLLM+bitsandbytes to llama.cpp+GGUF

bitsandbytes peak RAM ~54GB (bf16 load before quantize) — kills 40GB OptiPlex.
llama.cpp Q4_K_M GGUF loads pre-quantized: peak RAM ~15.5GB, fits cleanly.

Changes:
- serve.yml: build llama.cpp with CUDA, download Q4_K_M GGUF from bartowski,
  disable vllm-serve, deploy llama-server.service
- llama-server.service.j2: OpenAI-compatible server on same port 8000,
  --n-gpu-layers 99 (full GPU offload), --parallel 4, gemma chat template
- defaults: llm_gguf_dir, llm_gguf_path, llm_gpu_layers, llm_parallel_slots
- handlers: restart llama-server, vllm-serve failed_when=false (may not exist)

GGUF: bartowski/gemma-2-27b-it-Q4_K_M.gguf (15.5GB, 24GB VRAM fits w/ ~8GB headroom)

ansible/roles/llm-inference/templates/llama-server.service.j2

@@ -0,0 +1,27 @@
+[Unit]
+Description=llama-server — Gemma 2 27B-it Q4_K_M (OpenAI-compatible inference)
+After=network.target nvidia-persistenced.service
+Wants=nvidia-persistenced.service
+
+[Service]
+Type=simple
+User={{ llm_venv_owner }}
+Group={{ llm_venv_owner }}
+Environment="HOME=/home/{{ llm_venv_owner }}"
+ExecStart=/opt/llama.cpp/build/bin/llama-server \
+    --model {{ llm_gguf_path }} \
+    --host {{ llm_serve_host }} \
+    --port {{ llm_serve_port }} \
+    --ctx-size {{ llm_max_model_len }} \
+    --n-gpu-layers {{ llm_gpu_layers }} \
+    --parallel {{ llm_parallel_slots }} \
+    --chat-template gemma
+Restart=on-failure
+RestartSec=10
+TimeoutStartSec=120
+StandardOutput=journal
+StandardError=journal
+SyslogIdentifier=llama-server
+
+[Install]
+WantedBy=multi-user.target