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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 These pages are historical reference.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

vLLM Architecture

Model Deployment Guides

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