docs: rename docs/ -> serverAppConfigs-docs/ per system-wide PROJECTNAME-docs convention

All in-repo path references (AGENTS.md, README, old scripts, report artifacts,
test code paths) rewritten in the same commit; focused-test map extended for
.gitignore, old/scripts/*, dankclaw/comfyui/*, and report/* per this repo's
own convention ('add one before committing'). Obsidian wikilinks are
title-based and unaffected.

serverAppConfigs-docs/LLM/VLLM Index.md

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+# vLLM Index
+
+> **RETIRED (2026-08-18).** The vLLM service on Dankclaw was replaced by the CIRU ROCmFPX llama.cpp runtime (v3). These pages are historical reference. See [[ciru-activefpx-promptforge]].
+
+## Documentation Overview
+
+This index contains all documentation for vLLM deployment on Dankserver and Dankclaw.
+
+## Hardware Guides
+
+- [[Dankserver System Specs]] — NVIDIA GPU server specifications
+- [[Dankclaw System Specs]] — AMD GPU server specifications
+
+## vLLM Architecture
+
+- [[VLLM Overview]] — Complete vLLM documentation including installation, CLI, API, and key arguments
+- [[RAM CPU Offloading in VLLM]] — CPU RAM offloading mechanisms, swap space, and memory configuration
+- [[Hybrid CPU GPU Processing Dankserver]] — NVIDIA GPU with system RAM hybrid processing
+- [[Shared Memory Architecture Dankclaw]] — AMD ROCm unified memory and APU configuration
+
+## Model Deployment Guides
+
+- [[Running Qwen3.6 35B A3B FP8 on Dankserver]] — Qwen3.6-35B-A3B FP8 deployment on NVIDIA GPU
+- [[Running Qwen3.6 27B FP8 on Dankclaw]] — Qwen3.6-27B FP8 deployment on AMD GPU
+
+## Quick Reference
+
+### Installation Commands
+
+```
+# NVIDIA GPU
+uv pip install vllm --torch-backend=auto
+
+# AMD ROCm
+uv pip install vllm --extra-index-url https://wheels.vllm.ai/rocm/
+
+# CPU
+uv pip install vllm --extra-index-url https://wheels.vllm.ai/nightly/cpu/
+```
+
+### Server Start Commands
+
+```
+# Basic server
+vllm serve Qwen/Qwen2.5-1.5B-Instruct
+
+# With tensor parallelism
+vllm serve meta-llama/Llama-3.1-70B-Instruct --tensor-parallel-size 4
+
+# With quantization
+vllm serve Qwen/Qwen2.5-1.5B-Instruct --quantization fp8
+
+# With GPU memory control
+vllm serve Qwen/Qwen2.5-1.5B-Instruct --gpu-memory-utilization 0.8 --swap-space 8
+```
+
+### Common API Endpoints
+
+| Endpoint | Method | Description |
+|----------|--------|-------------|
+| /v1/models | GET | List available models |
+| /v1/completions | POST | Text completion |
+| /v1/chat/completions | POST | Chat completion |
+| /health | GET | Health check |
+| /metrics | GET | Prometheus metrics |
+
+### Key Parameters
+
+| Parameter | Description | Typical Value |
+|-----------|-------------|---------------|
+| tensor_parallel_size | Number of GPUs for model sharding | 1-8 |
+| gpu_memory_utilization | Fraction of GPU memory for KV cache (default 0.92) | 0.7-0.95 |
+| swap_space | Removed in current vLLM; use `--kv-offloading-size` | n/a |
+| max_model_len | Maximum context length | auto |
+| max_num_batched_tokens | Max tokens per batch | 2048-8192 |
+| max_num_seqs | Max sequences per batch | 128-512 |
+
+### Quantization Methods
+
+| Method | GPU Support | Description |
+|--------|-------------|-------------|
+| FP8 | Hopper, Ada (native); Ampere W8A16 via Marlin; RDNA via emulation | FP8 E4M3 inference |
+| INT8 | Most GPUs | Weight-only INT8 |
+| GPTQ | Most GPUs (NVIDIA; limited on ROCm) | Post-training GPTQ |
+| AWQ | Turing+ (NVIDIA) | Activation-aware weight quantization |
+| GGUF | CPU, GPU | General GPU unification format |
+
+## See Also
+
+- [[VLLM Overview]]
+- [[RAM CPU Offloading in VLLM]]
+- [[Hybrid CPU GPU Processing Dankserver]]
+- [[Shared Memory Architecture Dankclaw]]
+- [[Running Qwen3.6 35B A3B FP8 on Dankserver]]
+- [[Running Qwen3.6 27B FP8 on Dankclaw]]