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/defaults/main.yml

@@ -317,6 +317,26 @@ llm_swapmode_models:
     sleep_idle_seconds: -1 # never idle (always ready for embeddings)
     load_on_startup: true
 
+  # Added t_c5cef2b2 (2026-08-19, War Machine): Qwen3-8B aux model.
+  # Dense 8B (not MoE). ~4.68GB weights at Q4_K_M. GPU-resident (~5.2GB total
+  # including KV at 32K ctx). Thinking mode on by default — callers MUST send
+  # /no_think prefix for latency-sensitive aux tasks. See INI template comments.
+  # VRAM budget: Qwen3.8-27B (~17,804 MiB @ 65536 ctx) + Qwen3-8B (~5,300 MiB
+  # @ 32K ctx) = ~23,104 MiB — fits in 24GB (24,576 MiB) with ~1.4GB headroom.
+  # llama-swap evicts Qwen3.8 when Qwen3-8B is requested (cannot co-reside).
+  # LRU eviction is the operative strategy; cold load ~5s for 5GB GGUF.
+  - id: Qwen3-8B-Q4_K_M
+    gguf_path: "{{ llm_models_dir }}/Qwen3-8B-Q4_K_M.gguf"
+    port: 8106
+    n_gpu_layers: -1       # -1 = auto-detect / all layers to GPU (~5GB, fits easily)
+    ctx_size: 32768
+    batch_size: 4096
+    ubatch_size: 4096
+    parallel: 1
+    cache_type: q4_0
+    flash_attn: true
+    sleep_idle_seconds: 60  # idle after 60s no requests
+
 # llama-swap matrix routing configuration
 # Each row defines a set of models that can be co-resident and hot-swappable
 # Syntax: "model1 & model2" = both models in same row (via v250 expression DSL)
@@ -335,3 +355,6 @@ llm_swapmode_matrix_rows:
 
   - row: row4
     expr: "Phi-3.5-mini-instruct-Q8_0 & nomic-embed-text-v1.5"      # Mini + embed
+
+  - row: row5
+    expr: "Qwen3-8B-Q4_K_M & nomic-embed-text-v1.5"                  # Aux 8B + embed

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 }}
+