What is vLLM?

vLLM is a high-throughput and memory-efficient inference and serving engine for Large Language Models (LLMs). It focuses on making LLM serving faster and easier to use.

What are the key features of vLLM?

Key features include PagedAttention (efficient memory management), continuous batching (high throughput), CUDA/HIP graph optimization (fast execution), quantization support (GPTQ, AWQ, SqueezeLLM), seamless Hugging Face integration, and an OpenAI-compatible API.

What models does vLLM support?

vLLM supports a wide range of Hugging Face models, including Aquila, Baichuan, BLOOM, ChatGLM, DeciLM, Falcon, GPT-2, GPT BigCode, GPT-J, GPT-NeoX, InternLM, LLaMA & LLaMA-2, Mistral, Mixtral, MPT, OPT, Phi, Qwen, StableLM, and Yi.

How do I install vLLM?

You can install vLLM using pip: pip install vllm.

What is PagedAttention?

PagedAttention is a memory management technique used in vLLM that divides the attention key and value memory into pages. This allows for more efficient memory usage, especially for long sequences and large models.

What is continuous batching in vLLM?

Continuous batching is a technique where vLLM groups incoming requests together to maximize GPU utilization and increase throughput.

How does vLLM integrate with Hugging Face models?

vLLM seamlessly integrates with Hugging Face models, simplifying the deployment and serving process. This eliminates the need for complex model conversions.

Does vLLM support distributed inference?

Yes, vLLM supports tensor parallelism for distributed inference, allowing you to distribute large models across multiple GPUs for faster processing.

Is there an API for vLLM?

Yes, vLLM provides an OpenAI-compatible API server, making it easy to integrate with existing applications and tools.

How does UBOS enhance vLLM’s capabilities?

UBOS is a full-stack AI Agent development platform that allows you to orchestrate vLLM-powered AI Agents, connect them with enterprise data, build custom AI Agents, and deploy them at scale.