Files
serverAppConfigs/docs/LLM/index.md
T
Frank 7ca9dc248e frank-default-large: swap to caf-eemin Qwen3.8-27B ROCmI4 embedded-MTP
Replace the retired kingjones ROCmFP4 STRIX MTP payload on Dankclaw with
cafonz/Qwen3.8-27B-ROCmI4-MTP-GGUF (eemin namespace, public), served by the
charlie12345/ROCmFPX llama.cpp build (ROCmI4 W4A4, c49ebdbd) on the Strix Halo
iGPU. Q4_0_ROCMI4 with an embedded NextN/MTP head (no separate draft file);
strict-Qwen MTP n16/p0.60/backend sampling on ROCm0. Binary
/home/dank/rocmi4-qwen38/ROCmFPX/build-rocmi4-w4a4/bin/llama-server; model dir
/home/dank/models/eemin-Qwen3.8-27B-ROCMI4-MTP. Updated serve script + env,
ubatch bench, large-config bats test, docs, AGENTS.md.
2026-08-25 03:33:29 -07:00

5.0 KiB

LLM Fleet — Knowledge Base

Documentation for the LLM inference fleet on two local machines (2026-08-24): upstream llama.cpp on Dankserver (frank-default-small, Ornith-1.5-35B-A3B CRACK) and CIRU ROCmFPX llama.cpp on Dankclaw (frank-default-large, kingjones Qwen3.8-27B ROCmFP4). Both services run the Qwen-Sharp chat template.

2026-08-22: the former vllmScripts repository is merged into the serverAppConfigs repository. This knowledge base moved from the old repo's docs/ root to docs/LLM/. The fleet scripts and units now live in the repo's scripts/ directory.

Deployment Topology

graph LR
    subgraph Dankserver[NVIDIA Dankserver - Ubuntu 26.04]
        A[RTX 3090 24GB + 62GB RAM]
        B[upstream llama.cpp b10121
frank-default-small, port 7770]
        C[Ornith-1.5-35B-A3B-CRACK Q6_K
ngram-mod, selected experts offloaded via override, 491520 ctx]
        A --> B --> C
    end

    subgraph Dankclaw[AMD Strix Halo - Ubuntu 26.04]
        D[gfx1151 iGPU 64 GiB unified memory]
        E[CIRU ROCmFPX llama.cpp PromptForge v3
frank-default-large, port 7770]
        F[Qwen3.8-27B-Uncensored-ROCmFP4 Q6_0 AGENT
MTP draft + Sharp template, 262K ctx / 1 slot]
        D --> E --> F
    end

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

Servers

Server GPU VRAM / Memory OS
Dankserver System Specs NVIDIA RTX 3090 24 GB VRAM, 62 GB RAM Ubuntu 26.04
Dankclaw System Specs AMD Radeon 8060S (Strix Halo) gfx1151 unified memory Ubuntu 26.04

Active Deployment

  • deployment — deploy and update process for both servers
  • frank-redo-2026-08-17 — the 2026-08-17 topology change
  • eemin-rocmi4-mtp — the caf-eemin Qwen3.8-27B ROCmI4 embedded-MTP model on Dankclaw (frank-default-large, active 2026-08-25)
  • dealign-ornith-crack — the dealign Ornith-1.5-35B-A3B CRACK model on Dankserver (frank-default-small, active 2026-08-24)
  • kingjones-strix-mtp — the retired kingjones Qwen3.8-27B ROCmFP4 STRIX MTP model on Dankclaw (superseded 2026-08-25 by the ROCmI4 embedded-MTP model)
  • dealign-ornith-crack — the dealign Ornith-1.5-35B-A3B CRACK model on Dankserver (frank-default-small, active 2026-08-24)
  • ciru-activefpx-promptforge — the RETIRED v3 CIRU PromptForge runtime on Dankclaw (superseded 2026-08-24 by the kingjones ROCmFP4 model)
  • kairic-edge — the retired Kairic Edge IU4 experiment (deployed and reverted 2026-08-22)
  • Dankserver System Specs — the upstream llama.cpp box (frank-default-small)
  • Dankclaw System Specs — the CIRU ROCmFPX box (frank-default-large)

CIRU Reference (Dankclaw)

  • ciru-activefpx-promptforge — the RETIRED v3 model, ROCm 7.15 toolchain, and build history (on Dankclaw; the slot now serves the kingjones ROCmFP4 model, see kingjones-strix-mtp)
  • kairic-edge — Kairic Edge IU4 runtime: build runbook, binary identity, and experiment summary (retired 2026-08-22)

DFlash2 Reference (archived)

vLLM Reference (historical)

Memory Management

Retired Topology (2026-08-17 redo)

Pipeline Parallelism (archive)

  • Pipeline Parallelism/index — index of multi-node inference research. The PP pair is retired. These pages are research history.
  • Project knowledge base — the Podman app stacks on the same two hosts. OpenWebUI consumes the :7770 endpoint on Dankserver. The Gitea instance on Dankclaw hosts this repository.
  • Deployment Flow — the unified git-based deploy for both workloads