fix(llm-inference): bitsandbytes int4 OOM — pending switch to llama.cpp+GGUF

bitsandbytes quantizes on-the-fly: loads full bf16 weights (~54GB RAM peak)
before compressing to int4. Kills the 40GB OptiPlex on torch.compile warmup.

Fix in next commit: switch serve phase to llama.cpp + GGUF Q4_K_M.
Pre-quantized weights load directly — peak RAM ~16GB, no compile overhead.

ansible/roles/llm-inference/templates/vllm-serve.service.j2

@@ -14,6 +14,7 @@ ExecStart={{ llm_venv_path }}/bin/python -m vllm.entrypoints.openai.api_server \
     --model {{ llm_hf_model }} \
     --host {{ llm_serve_host }} \
     --port {{ llm_serve_port }} \
+    --quantization {{ llm_quantization }} \
     --gpu-memory-utilization {{ llm_gpu_memory_utilization }} \
     --max-model-len {{ llm_max_model_len }} \
     --enable-prefix-caching