Files
serverAppConfigs/docs/index.md
T
Frank 9544c36afc PP kit + Strix research: Qwen3.8-27B RPC split (Dankclaw head / Dankserver worker)
- scripts: serve-pp-head/worker, envs, systemd units, pp-test.sh orchestrator, pp-bench.py
- head design verified in b10433 source + live 4B smoke test:
  * no --device with --rpc (RPC device silently dropped otherwise)
  * default device selection = [RPC0, ROCm0] (HIP registers before Vulkan)
  * default split proportional to reported free mem = ~20/80 here, pin 1,1
  * -dio required for large model over RPC
- docs: qwen3.8-strix-optimizations (ranked no-quant-downgrade levers,
  quant landscape, 2026-08-16 test matrix), PP deployment runbook,
  index updates, Dankclaw Improvements corrections (Vulkan0, no power_profile sysfs)
- worker built at 9b05354ec in /home/dank/llama-cpp-rpc worktree (CUDA+RPC)
- second head build build-vulkan-rpc (HIP off) for backend A/B
2026-08-16 05:40:27 -07:00

3.8 KiB

vLLM Scripts — Knowledge Base

Documentation for running vLLM inference on two local machines.

Deployment Topology

graph LR
    subgraph Dankserver[NVIDIA Dankserver - Ubuntu 24.04]
        A[RTX 3090 24GB + 62GB RAM]
        B[llama.cpp ngram-mod
frank-default-small]
        C[Qwen3.6-35B-A3B uncensored Q6_K_P
MoE experts on CPU, port 7770]
        A --> B --> C
    end

    subgraph Dankclaw[AMD Strix Halo - Ubuntu 26.04]
        D[gfx1151 APU 30GiB phys / 96GiB GPU-visible]
        E[llama.cpp --spec-default ngram-mod
frank-default-large]
        F[Qwen3.8-27B UD-Q8_K_XL
Vulkan0 backend, port 7770, PARALLEL=2]
        D --> E --> F
    end

    G[LLM Clients / API] -->|HTTP :7770| B
    G -->|HTTP :7770| E

Servers

Server GPU VRAM / Memory OS
Dankserver System Specs NVIDIA RTX 3090 24 GB VRAM, 62 GB RAM Ubuntu 24.04
Dankclaw System Specs AMD Radeon 8060S (Strix Halo) 30 GiB physical / 96 GiB GPU-visible Ubuntu 26.04

vLLM Reference

  • VLLM Overview — installation, CLI, API, architecture, quantization, parallelism
  • VLLM Index — quick reference with commands, endpoints, and parameter table
  • deployment — deploy and update process for both servers

Memory Management

Model Deployment

Speculative Decoding & Quantization

Pipeline Parallelism