# vLLM Index

> **RETIRED (2026-08-18).** The vLLM service on Dankclaw was replaced by the CIRU ROCmFPX llama.cpp runtime (v3). These pages are historical reference. See [[ciru-activefpx-promptforge]].

## 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]] — Qwen3.6-35B-A3B FP8 deployment on NVIDIA GPU
- [[Running Qwen3.6 27B FP8 on Dankclaw]] — Qwen3.6-27B 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 (default 0.92) | 0.7-0.95 |
| swap_space | Removed in current vLLM; use `--kv-offloading-size` | n/a |
| max_model_len | Maximum context length | auto |
| max_num_batched_tokens | Max tokens per batch | 2048-8192 |
| max_num_seqs | Max sequences per batch | 128-512 |

### Quantization Methods

| Method | GPU Support | Description |
|--------|-------------|-------------|
| FP8 | Hopper, Ada (native); Ampere W8A16 via Marlin; RDNA via emulation | FP8 E4M3 inference |
| INT8 | Most GPUs | Weight-only INT8 |
| GPTQ | Most GPUs (NVIDIA; limited on ROCm) | Post-training GPTQ |
| AWQ | Turing+ (NVIDIA) | Activation-aware weight quantization |
| GGUF | CPU, GPU | General GPU unification format |

## See Also

- [[VLLM Overview]]
- [[RAM CPU Offloading in VLLM]]
- [[Hybrid CPU GPU Processing Dankserver]]
- [[Shared Memory Architecture Dankclaw]]
- [[Running Qwen3.6 35B A3B FP8 on Dankserver]]
- [[Running Qwen3.6 27B FP8 on Dankclaw]]