feat(astro-orbiter): add Qwen3-8B-Q4_K_M to inference stack (t_c5cef2b2)

- host_vars/astro-orbiter/vars.yml:
  - Stage Qwen3-8B-Q4_K_M GGUF (bartowski/Qwen_Qwen3-8B-GGUF, 5,027,784,224 bytes)
  - Fix stale VRAM comments: KV @ 128K ctx -> 65536 ctx (rolled back t_c9fed26c 2026-08-18)
  - Update worst-case VRAM table to include new 6th model

- defaults/main.yml:
  - Add Qwen3-8B-Q4_K_M to llm_swapmode_models (port 8106, GPU, 32K ctx, q4_0 KV)
  - Add row5 to llm_swapmode_matrix_rows (Qwen3-8B & nomic-embed)

- templates/llama-server-router-preset.ini.j2:
  - Add [Qwen3-8B-Q4_K_M] section (32K ctx, GPU, flash-attn, q4_0 KV, 60s idle evict)
  - Document thinking-mode handling: enabled by default; /no_think at call time for aux tasks

VRAM math: Qwen3-8B ~4.68GB weights + ~0.5GB KV @ 32K = ~5.2GB. Fits with nomic-embed
(~84MB) well within 24GB. Cannot co-reside with Qwen3.8-27B (17.8GB); LRU eviction applies.

Note: war-machine profile config.yaml also updated (Qwen3.6 stale entry -> Qwen3.8-27B;
added Qwen3-8B-Q4_K_M context_length: 32768). config.yaml not tracked in git.

ansible/roles/llm-inference-multimodel/templates/llama-server-router-preset.ini.j2

@@ -207,3 +207,46 @@ rope-scaling       = yarn
 rope-freq-scale    = 0.75
 load-on-startup    = true
 sleep-idle-seconds = -1
+
+; --- Aux model: Qwen3-8B-Q4_K_M -----------------------------------------------
+; Added t_c5cef2b2 (2026-08-19, War Machine).
+; Qwen3-8B is a dense 8B model (Qwen3 family, Alibaba) for aux tasks:
+; intent classification, query rewriting, structured extraction, tool-call
+; construction, and draft generation. Chosen over Llama-3.1-8B for superior
+; json_schema grammar support and stronger instruction-following.
+;
+; Architecture: 36 layers, 32 Q heads, 8 KV heads (GQA), 32K native context,
+; 131K via YaRN. Q4_K_M GGUF from bartowski/Qwen_Qwen3-8B-GGUF. ~4.68GB weights.
+;
+; ctx-size=32768: native training context (safe, no YaRN extension needed).
+; VRAM at 32K ctx with q4_0 KV: ~4.68GB weights + ~0.5GB KV ≈ 5.2GB total.
+; Fits comfortably on RTX 3090 24GB. Co-resident with nomic-embed (~84MB):
+; ~5.3GB total — well within 24GB budget even as a warm auxiliary model.
+;
+; THINKING MODE NOTE (JARVIS clarification, t_c5cef2b2):
+;   Qwen3-8B has thinking (chain-of-thought) mode ENABLED BY DEFAULT.
+;   For latency-sensitive aux tasks (routing, rewriting, structured extraction),
+;   disable at call time — NOT in this deployment config:
+;     - Prompt prefix: begin the user message with "/no_think"
+;     - Chat template override: pass enable_thinking=False in the template vars
+;       (llama.cpp Jinja2 template: {% if enable_thinking is false %} ... )
+;   Do NOT hardcode thinking=false here — this is a call-site concern.
+;   Thinking mode IS appropriate for complex multi-step tool plans and long-context
+;   summarization; leave that decision to the caller.
+;
+; Source: bartowski/Qwen_Qwen3-8B-GGUF (public, no auth). Stored locally as
+; Qwen3-8B-Q4_K_M.gguf (canonical name, trimmed from HF's Qwen_Qwen3-8B prefix).
+; n-gpu-layers=99: GPU (all layers). At ~5GB, fully GPU-resident with headroom.
+; flash-attn=true: Qwen3-8B uses standard attention; flash-attn is safe.
+; sleep-idle-seconds=60: evict after 60s idle to free GPU VRAM for Qwen3.8-27B.
+[Qwen3-8B-Q4_K_M]
+model        = {{ llm_models_dir }}/Qwen3-8B-Q4_K_M.gguf
+n-gpu-layers = 99
+ctx-size     = 32768
+flash-attn   = true
+cache-type-k = {{ llm_router_cache_type_k }}
+cache-type-v = {{ llm_router_cache_type_v }}
+batch-size   = {{ llm_router_batch_size }}
+ubatch-size  = {{ llm_router_ubatch_size }}
+parallel     = {{ llm_router_parallel }}
+