Environment:LLMBook zh LLMBook zh github io VLLM Inference Environment
| Knowledge Sources | |
|---|---|
| Domains | Infrastructure, Inference, LLMs |
| Last Updated | 2026-02-08 04:30 GMT |
Overview
vLLM inference engine environment for high-throughput LLM text generation with PagedAttention.
Description
This environment provides the vLLM library for efficient large language model inference. vLLM uses PagedAttention to manage GPU memory for KV-cache, enabling high-throughput batch inference. The codebase uses vllm.LLM for model initialization and vllm.SamplingParams for controlling decoding behavior. The example demonstrates inference with LLaMA-2-7B-Chat.
Usage
Use this environment for any batch inference or text generation workflow. It is the mandatory prerequisite for the VLLM_LLM_Generate implementation. Use vLLM when you need high-throughput inference with multiple prompts simultaneously, rather than the standard Hugging Face generate pipeline.
System Requirements
| Category | Requirement | Notes |
|---|---|---|
| OS | Linux | vLLM has limited Windows/Mac support |
| Hardware | NVIDIA GPU | Minimum 16GB VRAM for 7B model inference |
| Python | Python >= 3.8 | Required by vLLM |
| Disk | 30GB+ | For cached model weights |
Dependencies
Python Packages
- `vllm` >= 0.2.0
Credentials
- `HF_TOKEN`: Hugging Face API token for gated model access (e.g., LLaMA-2 models).
Quick Install
# Install vLLM
pip install vllm
# Verify installation
python -c "import vllm; print('vLLM installed successfully')"
Code Evidence
vLLM model initialization from `code/9.1 vLLM实践.py:1,11`:
import vllm
model = vllm.LLM(model='meta-llama/Llama-2-7b-chat-hf')
Sampling parameters configuration from `code/9.1 vLLM实践.py:14-19`:
sampling_params = vllm.SamplingParams(
temperature=0,
max_tokens=2048,
presence_penalty=0,
frequency_penalty=0,
)
Batch generation from `code/9.1 vLLM实践.py:22`:
out = model.generate(prompts, sampling_params=sampling_params)
Common Errors
| Error Message | Cause | Solution |
|---|---|---|
| `ImportError: No module named 'vllm'` | vLLM not installed | `pip install vllm` |
| `CUDA out of memory` | Insufficient VRAM for model | Use a smaller model or reduce `gpu_memory_utilization` |
| `ValueError: Model not found` | Model path incorrect or gated | Check model path and set `HF_TOKEN` for gated models |
Compatibility Notes
- GPU Memory: vLLM pre-allocates GPU memory for KV-cache. Default `gpu_memory_utilization=0.9` may need reduction on smaller GPUs.
- Model Format: vLLM supports HuggingFace format models. GGUF and other formats require conversion.
- Prompt Format: LLaMA-2-Chat models require `[INST] ... [/INST]` prompt format as shown in the code example.