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/Running Qwen3.6 27B FP8 on Dankclaw.md

@@ -47,6 +47,8 @@ The FP8 variant requires approximately 16-17 GB. ThinkingCap-FP8 requires ~29 GB
 
 ## Prerequisites
 
+> **Production note:** Dankclaw's primary llama.cpp service now runs the ROCmFPX runtime with the Ling-3.0-flash ROCmFP4 MTP model (see `scripts/llama-server.service`). This page documents the alternative vLLM FP8 deployment of Qwen3.6-27B.
+
 Install vLLM for ROCm. Version 0.24.0+ is required for Qwen3.6 with speculative decoding:
 
 ```
@@ -55,7 +57,7 @@ source .venv/bin/activate
 uv pip install vllm --extra-index-url https://wheels.vllm.ai/rocm/
 ```
 
-Installed: vLLM 0.26.0+rocm723 in Python 3.12.13 venv at `/home/dank/vllm-env`.
+Installed: vLLM 0.26.0 in Python 3.12.13 venv at `/home/dank/vllm-rocm-venv`.
 
 ### MPI Dependency
 
@@ -198,7 +200,7 @@ VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 vllm serve Qwen/Qwen3.6-27B-FP8 \
 ## ROCm Notes
 
 - Strix Halo (gfx1151) is listed as a supported AMD GPU for vLLM
-- FP8 may use software emulation on RDNA architecture. Verify with a BF16 test first
+- FP8 uses **software emulation** on RDNA architecture (gfx1151 has no native FP8 tensor cores). This gives memory savings, not full compute speedup. Compare against a BF16 test first
 - Attention backends: try `ROCM_AITER_UNIFIED_ATTN` if the default backend underperforms
 - Context length on ROCm may be limited for some models. Start with 131072 and increase gradually