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