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 Overview.md

@@ -11,7 +11,7 @@ VLLM is fast with these features:
 - Chunked prefill and prefix caching
 - FP8, INT8, INT4, GPTQ, AWQ, and GGUF quantization
 - Tensor, pipeline, data, expert, and context parallelism
-- Speculative decoding with n-gram, suffix, and EAGLE
+- Speculative decoding with n-gram, suffix, EAGLE/EAGLE3, DFlash, DSpark, and MTP
 - Automatic kernel generation with torch.compile
 - Disaggregated prefill, decode, and encode
 
@@ -130,11 +130,11 @@ curl http://localhost:8000/v1/chat/completions \
 |----------|---------|-------------|
 | `tensor_parallel_size` | 1 | Number of GPUs for tensor parallelism |
 | `pipeline_parallel_size` | 1 | Number of GPUs for pipeline parallelism |
-| `gpu_memory_utilization` | 0.9 | Fraction of GPU memory for KV cache |
-| `swap_space` | 4 | CPU swap space in GiB |
+| `gpu_memory_utilization` | 0.92 | Fraction of GPU memory for KV cache |
+| `swap_space` | removed | Removed in current vLLM; use `--kv-offloading-size` |
 | `enforce_eager` | false | Use eager mode (no CUDA graphs) |
 | `max_num_batched_tokens` | 2048 | Max tokens per batch |
-| `max_num_seqs` | 256 | Max sequences per batch |
+| `max_num_seqs` | 128 | Max sequences per batch |
 | `block_size` | 16 | Token block size |
 | `num_gpu_blocks_override` | null | Override number of GPU blocks |
 | `max_model_len_per_seq` | null | Max length per sequence |
@@ -157,8 +157,8 @@ curl http://localhost:8000/v1/chat/completions \
 
 | Argument | Default | Description |
 |----------|---------|-------------|
-| `gpu_memory_utilization` | 0.9 | Fraction of GPU memory for KV cache |
-| `swap_space` | 4 | CPU swap space in GiB |
+| `gpu_memory_utilization` | 0.92 | Fraction of GPU memory for KV cache |
+| `swap_space` | removed | Removed in current vLLM; use `--kv-offloading-size` |
 | `kv_cache_dtype` | auto | KV cache data type (auto, fp8, fp8_e5m2) |
 | `max_model_len` | auto | Maximum context length |
 | `num_gpu_blocks` | auto | Number of GPU blocks |
@@ -204,18 +204,20 @@ VLLM supports over 200 model architectures including:
 
 VLLM supports these quantization methods:
 
-- FP8 for Hopper and Ada GPUs
+- FP8 for Hopper and Ada GPUs (native). Ampere runs FP8 W8A16 via Marlin; FP8 W8A8 computes via fallback
 - MXFP8/MXFP4 for Hopper GPUs
-- NVFP4 for Hopper GPUs
+- NVFP4 for Hopper and Blackwell GPUs
 - INT8 for most GPUs
 - INT4 for most GPUs
 - GPTQ for most GPUs
-- AWQ for Turing and newer GPUs
+- AWQ for Turing and newer GPUs (NVIDIA; native support varies on ROCm)
 - GGUF for CPU and GPU
 - Compressed-tensors for custom quantization
 - ModelOpt for NVIDIA Model Optimizer
 - TorchAO for PyTorch AO
 
+AMD RDNA 3 / RDNA 3.5 (gfx11, Strix Halo) run FP8 via software emulation, not native tensor cores. AWQ and GPTQ support in vLLM on AMD ROCm is limited; use GGUF or AMD Quark.
+
 ## Parallelism
 
 VLLM supports multiple parallelism strategies: