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/Dankserver System Specs.md

@@ -12,6 +12,7 @@ Dankserver is an NVIDIA GPU server for vLLM inference. The host runs Ubuntu 24.0
 |----------|-------|
 | Architecture | x86_64 |
 | Vendor | AuthenticAMD |
+| Model | AMD Ryzen 7 7700X |
 | Cores | 8 (1 socket) |
 | NUMA Nodes | 1 (node 0, 63503 MB) |
 
@@ -37,8 +38,8 @@ Dankserver is an NVIDIA GPU server for vLLM inference. The host runs Ubuntu 24.0
 
 | Filesystem | Size | Used | Avail | Use% |
 |------------|------|------|-------|------|
-| / (nvme0n1p2) | 915G | 849G | 20G | 98% |
-| /mnt/entropy (dm-0) | 91T | 86T | 977G | 99% |
+| / (nvme0n1p2) | 915G | 729G | 140G | 84% |
+| /mnt/entropy (dm-0) | 91T | 86T | 910G | 99% |
 
 ### OS and Software
 
@@ -50,14 +51,14 @@ Dankserver is an NVIDIA GPU server for vLLM inference. The host runs Ubuntu 24.0
 
 ### Active Services
 
-A llama-server process occupies approximately 20 GB of the RTX 3090 VRAM.
+A vLLM engine core (Podman container `vllm-qwen36-dflash`) occupies approximately 22 GB of the RTX 3090 VRAM (`nvidia-smi` reports `VLLM::EngineCore` at 22164 MiB).
 
 ## VLLM Relevance
 
 - 24 GB VRAM limits model size. Use FP8 quantization for large models.
 - 62 GB system RAM provides ample swap space for KV cache offloading.
 - 127 GB swap supports large `--swap-space` values for CPU KV cache paging.
-- Root disk has only 20 GB free. Store models on /mnt/entropy.
+- Root disk has approximately 140 GB free. Store models on /mnt/entropy.
 - CUDA 12.0 and Python 3.12.3 are compatible with vLLM.
 
 ## See Also