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/Shared Memory Architecture Dankclaw.md

@@ -84,13 +84,12 @@ Since memory is unified, set these parameters carefully:
 ```
 vllm serve model \
     --gpu-memory-utilization 0.85 \
-    --swap-space 8 \
     --max-num-batched-tokens 4096
 ```
 
 - `gpu_memory_utilization`: Set lower (0.8-0.85) to leave room for CPU operations
-- `swap_space`: Keep moderate (8-16 GB) for overflow
 - `max_model_len`: Match actual context needs
+- The old `--swap-space` argument was removed from current vLLM. Use `--kv-offloading-size` for CPU KV offload
 
 ### NUMA Binding
 
@@ -197,8 +196,10 @@ Some models on ROCm have context length limited to 4096 tokens. Check model docu
 FP8 quantization depends on GPU architecture:
 
 - MI300 series supports native FP8
-- Radeon RX 7900 supports FP8 via software
-- Strix Halo may support FP8 via software emulation
+- Radeon RX 7900 supports FP8 via software emulation
+- Strix Halo (gfx1151) uses **software emulation** for FP8 (no native FP8 tensor cores); expect memory savings, not full compute speedup
+
+For FP4 on Dankclaw, the ROCmFPX llama.cpp fork (`Q2_0_ROCMFP4`) provides native ROCm FP4 kernels.
 
 ## Example Commands