Frank
39db415348
- VLLM Overview: installation, CLI, API, architecture, quantization, parallelism - VLLM Index: quick reference with commands, endpoints, parameters - Dankserver System Specs: RTX 3090 24GB, 62GB RAM, Ubuntu 24.04 - Dankclaw System Specs: AMD Strix Halo APU, 61GB unified, ROCm 7.1 - RAM/CPU Offloading in VLLM: KV cache swap, gpu_memory_utilization, preemption - Hybrid CPU/GPU Processing (Dankserver): NVIDIA GPU + system RAM configuration - Shared Memory Architecture (Dankclaw): AMD unified memory and ROCm setup - Running Qwen3.6 35B A3B FP8 on Dankserver: MoE model deployment guide - Running Qwen3.6 27B FP8 on Dankclaw: dense model deployment guide
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VLLM Index
Documentation Overview
This index contains all documentation for VLLM deployment on Dankserver and Dankclaw.
Hardware Guides
- Dankserver System Specs -- NVIDIA GPU server specifications
- Dankclaw System Specs -- AMD GPU server specifications
VLLM Architecture
- VLLM Overview -- Complete VLLM documentation including installation, CLI, API, and key arguments
- RAM CPU Offloading in VLLM -- CPU RAM offloading mechanisms, swap space, and memory configuration
- Hybrid CPU GPU Processing Dankserver -- NVIDIA GPU with system RAM hybrid processing
- Shared Memory Architecture Dankclaw -- AMD ROCm unified memory and APU configuration
Model Deployment Guides
- Running Qwen3.6 35B A3B FP8 on Dankserver -- Qwen3-30B-A3B FP8 deployment on NVIDIA GPU
- Running Qwen3.6 27B FP8 on Dankclaw -- Qwen3-32B FP8 deployment on AMD GPU
Quick Reference
Installation Commands
# NVIDIA GPU
uv pip install vllm --torch-backend=auto
# AMD ROCm
uv pip install vllm --extra-index-url https://wheels.vllm.ai/rocm/
# CPU
uv pip install vllm --extra-index-url https://wheels.vllm.ai/nightly/cpu/
Server Start Commands
# Basic server
vllm serve Qwen/Qwen2.5-1.5B-Instruct
# With tensor parallelism
vllm serve meta-llama/Llama-3.1-70B-Instruct --tensor-parallel-size 4
# With quantization
vllm serve Qwen/Qwen2.5-1.5B-Instruct --quantization fp8
# With GPU memory control
vllm serve Qwen/Qwen2.5-1.5B-Instruct --gpu-memory-utilization 0.8 --swap-space 8
Common API Endpoints
| Endpoint | Method | Description |
|---|---|---|
| /v1/models | GET | List available models |
| /v1/completions | POST | Text completion |
| /v1/chat/completions | POST | Chat completion |
| /health | GET | Health check |
| /metrics | GET | Prometheus metrics |
Key Parameters
| Parameter | Description | Typical Value |
|---|---|---|
| tensor_parallel_size | Number of GPUs for model sharding | 1-8 |
| gpu_memory_utilization | Fraction of GPU memory for KV cache | 0.7-0.95 |
| swap_space | CPU swap space in GiB | 4-32 |
| max_model_len | Maximum context length | auto |
| max_num_batched_tokens | Max tokens per batch | 2048-8192 |
| max_num_seqs | Max sequences per batch | 128-256 |
Quantization Methods
| Method | GPU Support | Description |
|---|---|---|
| FP8 | Hopper, Ada, Ampere | Native FP8 inference |
| INT8 | Most GPUs | Weight-only INT8 |
| GPTQ | Most GPUs | Post-training GPTQ |
| AWQ | Turing+ | Activation-aware weight quantization |
| GGUF | CPU, GPU | General GPU unification format |