Reorganize: llm/docs -> docs/LLM (LLM fleet knowledge base)

The LLM fleet documentation (deployment runbook, model runbooks,
benchmarks, research archive, retired-topology history) joins the
project knowledge base as docs/LLM/. Wikilinks resolve by filename,
so no link updates are needed for the move.

docs/LLM/Dankserver System Specs.md

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+# Dankserver System Specs
+
+## Server Overview
+
+Dankserver is an NVIDIA GPU server for vLLM inference. The host runs Ubuntu 26.04 (Resolute Raccoon) 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% |
+
+### Network
+
+| Interface | Type | Speed | IP | Notes |
+|-----------|------|-------|----|-------|
+| eno1 | onboard | 2500 Mb/s (2.5 GbE) | 192.168.0.235 (DHCP) | RPC worker endpoint (port 50052). Default route via 192.168.0.1. |
+| enxe8ea6ab7ebca | USB-C class adapter | 2500 Mb/s | 192.168.0.252 | UNPLUGGED 2026-08-17. Was idle (unused by the RPC path). |
+| wlp8s0 | WiFi | - | - | Unused (carrier down). |
+| docker0 | bridge | - | 172.17.0.1 | Down; internal docker traffic only. |
+
+### OS and Software
+
+| Property | Value |
+|----------|-------|
+| OS | Ubuntu 26.04 LTS (Resolute Raccoon) |
+| Kernel | 7.0.0-30-generic |
+| Python | 3.14.4 |
+
+### Active Services
+
+The active service on Dankserver is `frank-pp-worker.service`: the ggml-rpc worker (RTX 3090) that receives the PP head's tensor share (45 of 65 blocks, ~23.8 GiB of 24 GiB at steady state). The old llama.cpp Qwen3.6-35B-A3B service (`frank-default-small`) is retired (2026-08-16) and disabled (2026-08-17). See [[Qwen3.8 PP Deployment]].
+
+## Hardware Relevance
+
+- 24 GB VRAM is close to saturated with this config. At idle ~22.1 GB used, leaving ~2.0 GiB headroom at `--n-cpu-moe 36` (4 layers' experts on GPU) with `ubatch 3072` and a dual-f16 KV pool of 13680 MiB at 700416 cells (2026-08-14 refit; the same pool at 800768 cells OOM'd the load warmup). A llama-bench sweep ([[frank-default-small-benchmark]]) guided this state: prefill is the priority, so `ubatch 3072` stays (top prefill tier), and `ncmoe 36` was raised from 35 to fix a cuBLAS OOM-class crash during big prefills by freeing ~0.75 GiB peak VRAM at only -2% prefill. If it still crashes under load, add `GGML_CUDA_FORCE_MMQ=1`. If decode speed matters more, use `ncmoe 32 - ubatch 1024` (gen ~48.7 t/s). The tuning guidance applied to the retired `frank-default-small` service; the PP worker's allocations are fixed at load.
+- 62 GB system RAM holds the ~29 GB of MoE expert weights with room to spare (~57 GB available after load).
+- 127 GB swap is available for CPU KV cache paging if GPU KV offload is disabled.
+- Root disk has approximately 140 GB free. Store the GGUF models under `/home/dank/models` (about 32 GB total).
+
+## See Also
+
+- [[VLLM Overview]]
+- [[Hybrid CPU GPU Processing Dankserver]]
+- [[RAM CPU Offloading in VLLM]]
+- [[Running Qwen3.6 35B A3B FP8 on Dankserver]]