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 35B A3B FP8 on Dankserver.md

@@ -49,6 +49,12 @@ Install vLLM. Version 0.19.0 or later is required for Qwen3.6:
 uv pip install vllm --torch-backend=auto
 ```
 
+## Hardware Quantization Notes (RTX 3090 / Ampere)
+
+The RTX 3090 is an Ampere GPU (compute capability 8.6). It has **no native FP8 tensor cores**. FP8 W8A8 compute runs through a fallback/dequantization path, so the FP8 model gives memory savings but not full compute speedup. vLLM loads these models and runs them; verify quality against a BF16 run. `--kv-cache-dtype fp8` is still valid on CUDA 12 for an FP8 KV cache.
+
+> Do not run on production without a BF16 comparison. The FP8 precision path may differ from native-FP8 hardware.
+
 ## vLLM Serve Commands
 
 ### Text-Only (Recommended for Memory Savings)
@@ -91,7 +97,7 @@ vllm serve Qwen/Qwen3.6-35B-A3B-FP8 \
 |-----------|-------|--------|
 | --kv-cache-dtype fp8 | FP8 KV cache | Cuts KV cache memory by 50% |
 | --gpu-memory-utilization 0.75 | 75% of VRAM | Leaves room for all MoE weights |
-| --swap-space 32 | 32 GB CPU swap | Dankserver has 62 GB RAM |
+| --swap-space 32 (removed) | 32 GB CPU overflow | Modern vLLM removed swap-space; use --kv-offloading-size |
 | --max-model-len 131072 | 128K context | Full 262K context is too large for 24 GB |
 | --max-num-batched-tokens 2048 | Conservative batch | Prevents OOM with MoE |
 | --reasoning-parser qwen3 | Qwen3 parser | Required for thinking mode output |
@@ -106,10 +112,10 @@ export HF_HOME=/mnt/entropy/huggingface
 
 ### Active Service Conflict
 
-A llama-server process occupies approximately 20 GB of VRAM. Stop it before starting vLLM:
+A vLLM engine core (Podman container `vllm-qwen36-dflash`) occupies roughly 22 GB of the 24 GB VRAM. Stop it before starting a competing service:
 
 ```
-pkill llama-server
+podman stop vllm-qwen36-dflash
 nvidia-smi  # verify GPU is free
 ```