feat: initialize vLLM scripts knowledge base

- VLLM Overview: installation, CLI, API, architecture, quantization, parallelism
- VLLM Index: quick reference with commands, endpoints, parameters
- Dankserver System Specs: RTX 3090 24GB, 62GB RAM, Ubuntu 24.04
- Dankclaw System Specs: AMD Strix Halo APU, 61GB unified, ROCm 7.1
- RAM/CPU Offloading in VLLM: KV cache swap, gpu_memory_utilization, preemption
- Hybrid CPU/GPU Processing (Dankserver): NVIDIA GPU + system RAM configuration
- Shared Memory Architecture (Dankclaw): AMD unified memory and ROCm setup
- Running Qwen3.6 35B A3B FP8 on Dankserver: MoE model deployment guide
- Running Qwen3.6 27B FP8 on Dankclaw: dense model deployment guide

docs/VLLM Index.md

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+# VLLM Index
+
+## 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]] -- Qwen3-30B-A3B FP8 deployment on NVIDIA GPU
+- [[Running Qwen3.6 27B FP8 on Dankclaw]] -- Qwen3-32B 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 | 0.7-0.95 |
+| swap_space | CPU swap space in GiB | 4-32 |
+| max_model_len | Maximum context length | auto |
+| max_num_batched_tokens | Max tokens per batch | 2048-8192 |
+| max_num_seqs | Max sequences per batch | 128-256 |
+
+### Quantization Methods
+
+| Method | GPU Support | Description |
+|--------|-------------|-------------|
+| FP8 | Hopper, Ada, Ampere | Native FP8 inference |
+| INT8 | Most GPUs | Weight-only INT8 |
+| GPTQ | Most GPUs | Post-training GPTQ |
+| AWQ | Turing+ | 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]]