# vLLM Scripts — Knowledge Base

Documentation for running vLLM inference on two local machines.

## Deployment Topology

```mermaid
graph LR
    subgraph Dankserver[NVIDIA Dankserver - Ubuntu 24.04]
        A[RTX 3090 24GB + 62GB RAM]
        B[llama.cpp DFlash
frank-default-small]
        C[Qwen3.6-35B-A3B uncensored Q6_K_P
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[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 :7770| B
    G -->|HTTP :7770| E
```

## Servers

| Server                                  | GPU                           | VRAM / Memory                       | OS           |
| --------------------------------------- | ----------------------------- | ----------------------------------- | ------------ |
| [[Dankserver System Specs\|Dankserver]] | NVIDIA RTX 3090               | 24 GB VRAM, 62 GB RAM               | Ubuntu 24.04 |
| [[Dankclaw System Specs\|Dankclaw]]     | 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