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

vLLM Scripts

Scripts for running large language models via vLLM on two local machines.

Targets

Host User Codename Architecture
dankserver dank DankServer NVIDIA GPU + System RAM
dankclaw dank DankClaw AMD Strix Halo (Shared Memory)

Models

Model Target Format
Qwen3.6 35B A3B FP8 dankserver FP8
Qwen3.6 27B FP8 dankclaw FP8

Structure

  • docs/ — Project knowledge base with research notes on vLLM configuration, hardware specifics, and deployment notes
  • scripts/ — Executable vLLM launch scripts (coming soon)

Knowledge Base

See docs/README.md for the project knowledge base index.

S
Description
Podman deployment configs for Dankserver and Dankclaw
Readme
8.3 MiB
Languages
Shell 60.4%
Python 21%
HTML 6.2%
Jinja 3.9%
C++ 3.9%
Other 4.6%