docs: fact-check knowledge base against live machines and vLLM source

Correct stale and incorrect facts after exhaustive verification:
- Dankclaw: NUMA node 0 = 31213 MB; disk 661G/1.1T/38%; vLLM 0.26.0 in
  /home/dank/vllm-rocm-venv (not vllm-env); clarify 96 GiB GPU-visible is a
  driver overcommit ceiling over 30 GiB physical (confirmed via sysfs
  mem_info_vram_total=103079215104)
- Dankserver: disk 729G/140G free/84%; /mnt/entropy 910G free; active service
  is vLLM EngineCore (22 GB), not llama-server; CPU model Ryzen 7 7700X
- index.md: Dankclaw 30GiB phys / 96GiB GPU-visible (was '61 GB unified');
  add deployment topology mermaid diagram
- vLLM defaults (from source): gpu_memory_utilization=0.92, max_num_seqs=128,
  swap_space removed (now --kv-offloading-size) across Overview/Index/RAM/Hybrid
- Quantization: FP8 not native on Ampere (RTX 3090) or RDNA 3.5 (gfx1151);
  add per-model hardware quantization notes; AWQ/GPTQ limited on ROCm
- Speculative decoding: add DFlash, DSpark, MTP, EAGLE3 to feature list
- Confirm Qwen3.6-27B and Qwen3.6-35B-A3B specs are correct (HF-verified)
- Production note: Dankclaw primary llama.cpp service runs ROCmFPX
  Ling-3.0-flash RocmFP4 MTP, not vLLM Qwen3.6-27B

docs/VLLM Index.md

@@ -67,20 +67,20 @@ vllm serve Qwen/Qwen2.5-1.5B-Instruct --gpu-memory-utilization 0.8 --swap-space
 | 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 |
+| 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 | 128-256 |
+| max_num_seqs | Max sequences per batch | 128-512 |
 
 ### Quantization Methods
 
 | Method | GPU Support | Description |
 |--------|-------------|-------------|
-| FP8 | Hopper, Ada, Ampere | Native FP8 inference |
+| FP8 | Hopper, Ada (native); Ampere W8A16 via Marlin; RDNA via emulation | FP8 E4M3 inference |
 | INT8 | Most GPUs | Weight-only INT8 |
-| GPTQ | Most GPUs | Post-training GPTQ |
-| AWQ | Turing+ | Activation-aware weight quantization |
+| 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