feat: tighten contracts with GPU fleet topology, model routing, Strix Halo details

litellm-health: added GPU fleet topology table, model routing map,
3-tier GPU checks (reachability, VRAM, temp, test inference)

litellm-self-heal: added GPU fleet reference, rules for GPU
unreachable and model not responding

Infrastructure topology now fully documented:
- amdpve: Strix Halo CPU (35B, 16 threads, 262K ctx, llama-server)
- llm-gpu: RTX 3090 24GB (Dense tier)
- ocu-llm: RTX 5070 12GB (MoE + Light tiers)

litellm-health.prose.md

@@ -12,6 +12,7 @@ description: >
 - public_url: string — The public LiteLLM URL (default: "https://litellm.sysloggh.net")
 - backend_host: string — Internal CT host to check containers (default: "192.168.68.116")
 - auth_host: string — Authentik server for OIDC (default: "192.168.68.11")
+- gpu_hosts: array — GPU inference hosts to check (default: ["192.168.68.8", "192.168.68.110", "192.168.68.15"])
 
 ## Returns
 
@@ -34,6 +35,23 @@ description: >
 - If /health/unified reports any non-healthy components, status reflects it
 - Checks cover at minimum: admin UI, API docs, OIDC, containers, unified health, nginx proxy
 
+## GPU Fleet Topology
+
+| Host | IP | Hardware | Models Served | Engine |
+|------|-----|----------|---------------|--------|
+| llm-gpu | 192.168.68.8 | NVIDIA RTX 3090 (24 GB) | gemma-4-12b (Dense) | llama-server Docker |
+| ocu-llm | 192.168.68.110 | NVIDIA RTX 5070 (12 GB) | qwen3.6-27B-code (MoE), all Light tier | llama-server Docker |
+| amdpve | 192.168.68.15 | AMD Strix Halo (CPU-only, -ngl 0) | ornith-1.0-35b (35B) | llama-server bare-metal |
+
+## Model Routing (LiteLLM → Router → GPU)
+
+| Model Request | Router Routes To |
+|--------------|-----------------|
+| `gemma-4-12b` | `GPU_DENSE_URL` → 192.168.68.8:8080 |
+| `qwen3.6-27B-code` | `GPU_MOE_URL` → 192.168.68.110:8080 |
+| `ornith-1.0-35b` | Router routes to 192.168.68.15:8080 |
+| `syslog-auto` | Auto-routed by router tier logic |
+
 ## Execution
 
 1. **Read parameters** — Use provided values or defaults
@@ -50,7 +68,16 @@ description: >
 5. **Check backend container health**:
    - SSH to {{backend_host}} → `docker ps` → verify all 6 containers are healthy
    - Containers: harness-litellm, harness-nginx, harness-router, harness-postgres, harness-redis, harness-dashboard
-6. **Check OIDC auth endpoint**:
+6. **Check GPU fleet health**:
+   - For each {{gpu_hosts}}:
+     - SSH or nvidia-smi check → verify GPU is reachable
+     - Check VRAM usage (warn if >90%)
+     - Check temperature (warn if >80°C)
+     - Verify model responds to a small test prompt
+7. **Check model inference** — Send test prompt to each model via LiteLLM API:
+   - POST /v1/chat/completions with model name → expect 200 + valid response
+   - Record token counts for baseline performance
+8. **Check OIDC auth endpoint**:
    - Verify auth.sysloggh.net resolves to {{auth_host}}
    - GET https://auth.sysloggh.net/ → expect login page
-7. **Compile and report** — Determine overall_status from individual check results
+9. **Compile and report** — Determine overall_status from individual check results

litellm-self-heal.prose.md

@@ -66,6 +66,14 @@ logged as a `[REPORT]` knowledge graph node for long-term trending.
 If a fix required another agent's help (e.g., Authentik restart), a relay
 message is sent to the responsible agent with full context.
 
+## GPU Fleet
+
+| Host | IP | Hardware | Models | Engine |
+|------|-----|----------|--------|--------|
+| amdpve | 192.168.68.15 | Strix Halo CPU | ornith-1.0-35b (16t, 262K ctx) | llama-server :8080 |
+| llm-gpu | 192.168.68.8 | RTX 3090 24GB | gemma-4-12b Dense | llama-server :8080 |
+| ocu-llm | 192.168.68.110 | RTX 5070 12GB | qwen3.6-27B-code MoE+Light | llama-server :8080 |
+
 ## Remediation Rules
 
 ### Rule 1: Container Not Healthy
@@ -82,6 +90,16 @@ Detect → immediate escalate (external dependency)
 ### Rule 4: Docs 404
 Detect → fix DOCS_URL env var → verify → log
 
+### Rule 5: GPU Unreachable
+Detect → SSH to GPU host fails or model not responding
+Fix → restart llama-server on host via SSH
+Escalate → immediately (downtime affects inference)
+
+### Rule 6: Model Not Responding
+Detect → test prompt via LiteLLM returns non-200
+Fix → restart llama-server on GPU host → verify
+Escalate → after 2 failed restarts
+
 ## Execution
 
 1. Run health check