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/tasks/main.yml

@@ -3,25 +3,34 @@
 # FILE: roles/llm-inference/tasks/main.yml
 # DESCRIPTION: Entry point — imports one task file per phase.
 #              Phases are additive; re-running the full playbook is always safe.
+#              Use --tags to run a specific phase subset:
+#                --tags foundation,driver,vllm,model,serve,integration,monitoring
 # ------------------------------------------------------------------------------
 
 # Phase 1 — Foundation
 - import_tasks: foundation.yml
+  tags: [foundation]
 
 # Phase 2 — Driver
 - import_tasks: driver.yml
+  tags: [driver]
 
 # Phase 3 — vLLM
 - import_tasks: vllm.yml
+  tags: [vllm]
 
 # Phase 4 — Model
 - import_tasks: model.yml
+  tags: [model]
 
 # Phase 5 — Serve
 - import_tasks: serve.yml
+  tags: [serve]
 
 # Phase 6 — Integration
 - import_tasks: integration.yml
+  tags: [integration]
 
 # Phase 7 — Monitoring
 - import_tasks: monitoring.yml
+  tags: [monitoring]

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

@@ -22,9 +22,11 @@
   become: true
   become_user: "{{ llm_venv_owner }}"
 
-- name: Install vLLM
+- name: Install vLLM and bitsandbytes
   ansible.builtin.pip:
-    name: vllm
+    name:
+      - vllm
+      - bitsandbytes
     state: present
     virtualenv: "{{ llm_venv_path }}"
   become: true