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Frank 3a64c8f3dd 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

2026-08-10 20:59:33 -07:00

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Dankserver System Specs

Server Overview

Dankserver is an NVIDIA GPU server for vLLM inference. The host runs Ubuntu 24.04 with an RTX 3090 and 62 GB of system RAM.

Hardware Specifications

CPU

Property	Value
Architecture	x86_64
Vendor	AuthenticAMD
Model	AMD Ryzen 7 7700X
Cores	8 (1 socket)
NUMA Nodes	1 (node 0, 63503 MB)

GPU

Property	Value
Model	NVIDIA GeForce RTX 3090
VRAM	24576 MiB (24 GB)
Driver	595.84
CUDA Version	13.2
CUDA Toolkit	12.0 (nvcc)

Memory

Property	Value
System RAM	62 GiB
Swap	127 GiB
/dev/shm	32 GiB

Storage

Filesystem	Size	Used	Avail	Use%
/ (nvme0n1p2)	915G	729G	140G	84%
/mnt/entropy (dm-0)	91T	86T	910G	99%

OS and Software

Property	Value
OS	Ubuntu 24.04.4 LTS (Noble Numbat)
Kernel	7.0.0-28-generic
Python	3.12.3

Active Services

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 approximately 140 GB free. Store models on /mnt/entropy.
CUDA 12.0 and Python 3.12.3 are compatible with vLLM.

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