60 lines
2.3 KiB
Markdown
60 lines
2.3 KiB
Markdown
# vLLM Scripts — Knowledge Base
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Documentation for running vLLM inference on two local machines.
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## Deployment Topology
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```mermaid
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graph LR
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subgraph Dankserver[NVIDIA Dankserver - Ubuntu 24.04]
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A[RTX 3090 24GB + 62GB RAM]
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B[llama.cpp DFlash
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frank-default-small]
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C[Qwen3.6-35B-A3B uncensored Q6_K_P
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experts on CPU, port 7770]
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A --> B --> C
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end
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subgraph Dankclaw[AMD Strix Halo - Ubuntu 26.04]
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D[gfx1151 APU 30GiB phys / 96GiB GPU-visible]
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E[llama.cpp DFlash draft
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frank-default-large]
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F[Qwen3.6-27B uncensored Q8_K_P
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DFlash draft, port 7770]
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D --> E --> F
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end
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G[LLM Clients / API] -->|HTTP :7770| B
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G -->|HTTP :7770| E
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```
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## Servers
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| Server | GPU | VRAM / Memory | OS |
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| --------------------------------------- | ----------------------------- | ----------------------------------- | ------------ |
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| [[Dankserver System Specs\|Dankserver]] | NVIDIA RTX 3090 | 24 GB VRAM, 62 GB RAM | Ubuntu 24.04 |
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| [[Dankclaw System Specs\|Dankclaw]] | AMD Radeon 8060S (Strix Halo) | 30 GiB physical / 96 GiB GPU-visible | Ubuntu 26.04 |
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## vLLM Reference
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- [[VLLM Overview]] — installation, CLI, API, architecture, quantization, parallelism
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- [[VLLM Index]] — quick reference with commands, endpoints, and parameter table
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- [[deployment]] — deploy and update process for both servers
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## Memory Management
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- [[RAM CPU Offloading in VLLM]] — KV cache swap between GPU VRAM and system RAM
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- [[Hybrid CPU GPU Processing Dankserver]] — NVIDIA GPU compute with system RAM offload
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- [[Shared Memory Architecture Dankclaw]] — AMD unified memory and ROCm configuration
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## Model Deployment
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- [[Running Qwen3.6 35B A3B FP8 on Dankserver]] — MoE model (35B total, 3B active) on RTX 3090
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- [[Running Qwen3.6 27B FP8 on Dankclaw]] — Dense model (27B) on Strix Halo APU
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- [[frank-default-large-benchmark]] — backend/spec benchmark that drove the Dankclaw llama.cpp deployment
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## Speculative Decoding & Quantization
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- [[DFlash DSpark Speculative Decoding]] — block diffusion (DFlash) and semi-autoregressive (DSpark) drafting on vLLM and llama.cpp
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- [[Quantization by Hardware Architecture]] — which quant format fits which GPU/CPU
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