Frank
903d54c389
Update target maps (AGENTS, README, deployment, deploy.sh) from the retired ROCmFPX llama.cpp stack to the active vLLM-ROCm CIRU INT4 service on port 7770 with MTP speculative decoding. Align Dankserver to the running vllm-awq-qwen36 (AWQ, port 34401). Refresh KB pages: Dankclaw system specs (gpu-mem-util 0.95, CIRU venv), quantization choices (INT4 primary, MXFP4 experiment), DFlash/DSpark Dankclaw section, and the KB index topology.
2.1 KiB
2.1 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[vLLM in Podman
vllm-awq-qwen36]
C[Qwen3.6-35B-A3B AWQ 4bit
port 34401]
A --> B --> C
end
subgraph Dankclaw[AMD Strix Halo - Ubuntu 26.04]
D[gfx1151 APU 30GiB phys / 96GiB GPU-visible]
E[vLLM-ROCm CIRU INT4
frank-default-large]
F[Ling-3.0-flash CIRU INT4 MTP
port 7770]
D --> E --> F
end
G[LLM Clients / API] -->|HTTP :34401| 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
- RAM CPU Offloading in VLLM — KV cache swap between GPU VRAM and system RAM
- Hybrid CPU GPU Processing Dankserver — NVIDIA GPU compute with system RAM offload
- Shared Memory Architecture Dankclaw — AMD unified memory and ROCm configuration
Model Deployment
- Running Qwen3.6 35B A3B FP8 on Dankserver — MoE model (35B total, 3B active) on RTX 3090
- Running Qwen3.6 27B FP8 on Dankclaw — Dense model (27B) on Strix Halo APU
Speculative Decoding & Quantization
- DFlash DSpark Speculative Decoding — block diffusion (DFlash) and semi-autoregressive (DSpark) drafting on vLLM and llama.cpp
- Quantization by Hardware Architecture — which quant format fits which GPU/CPU