Files

T

Frank f650fff0aa docs: polish pass — fix stale names, duplicates, branding, and README

- Normalize 'VLLM' to 'vLLM' in prose across 6 docs (32 occurrences; wikilink
  targets untouched)
- Remove duplicated Environment Variables block in 27B Dankclaw doc
- Update stale model refs Qwen3-30B-A3B -> Qwen3.6-35B-A3B in example commands
- Fix VLLM Index: -- separators to em-dashes; correct model names on links
- README: document active services (ROCmFPX llama-server on Dankclaw, vLLM
  AWQ on Dankserver), replace broken [[docs/README.md]] link with docs/index.md

2026-08-10 21:12:23 -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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