How to apply LLM in production

  • Introducing project vLLM

What is vLLM?

  • vLLM is a fast and easy-to-use library for LLM inference and serving.

Longer version…

vLLM is a high-performance, memory-efficient, and easy-to-use library for serving large language models (LLMs) in production. It is designed to optimize the inference of LLMs, making it easier to deploy and scale them in real-world applications.

“vLLM is to LLM serving what TensorRT was to DL inference: high performance, low frills, and production first.”

What does vLLM good at/ Why I care about vLLM?

vLLM matters because it makes LLM serving fast, memory-efficient, and production-ready—without sacrificing flexibility.

High Troughput, Low Latency

Thanks to feature like PagedAttention and continuous batching, vLLM achieves up to 23x throughput with reduced latency compared to naive serving methods.

Plug-and-Play HuggingFace Integration

Supports most popular HuggingFace models without code changes.

Smart Memory Efficiency

PagedAttention manages KV cache memory more efficiently, allowing larger batch sizes and longer contexts without running out of GPU RAM.

Production-Ready Features

  • Speculative decoding
  • Prefix caching
  • Streaming output
  • Multi-LoRA support
  • OpenAI-compatible API server (as OpenAI API is industry standard right now)

Cost-Effective Inference, Multi-Hardware Support

  • Quantization support: (GPTQ, AWQ, INT4, FP8, etc.)
  • Hardware: support across NVIDIA, AMD, Intel, TPU, AWS Trainium, it helps you save money on inference.

Production-ready Deployment

Comes with Kubernetes, Docker, Nginx guides and monitoring hooks, ideal for serving LLMs at scale.

vLLM vs Other Inference Engines

LMCache

  • Focus: Token reuse for inference-time acceleration
  • Strength: Retrieval-based KV cache hit optimization
  • Weakness: Higher complexity to tune for low-redundancy tasks
  • Link

MoonCake (KVCache-AI)

  • Focus: Efficient pooling and KV memory reuse
  • Strength: Flexible cache design via pooling abstraction
  • Weakness: Smaller community, less mature
  • Link

Nixl (by NVIDIA Dynamo)

  • Focus: Unified backend with high-performance data transfer APIs
  • Strength: Custom hardware acceleration, optimized memory/messaging
  • Weakness: Requires deep NVIDIA ecosystem involvement
  • Link