vLLM

vLLM solves the computational bottleneck of deploying large language models at scale. Modern LLMs demand significant memory and compute resources, making practical inference expensive and slow. vLLM's optimized attention mechanism and dynamic batching enable organizations to serve models faster and more cost-effectively than traditional approaches.

Highlights

• PagedAttention optimization virtualizes attention key-value cache to reduce GPU memory usage
• Continuous batching and request scheduling maximize GPU utilization and throughput
• Supports 200+ model architectures including multimodal and mixture-of-experts variants
• Seamless Hugging Face integration with minimal code changes required
• Compatible across NVIDIA and AMD GPUs, Intel CPUs, and emerging hardware platforms
• Includes OpenAI-compatible REST API for straightforward deployment

Whether you're a researcher prototyping new models, an ML engineer deploying inference workloads in production, or an organization optimizing costs, vLLM provides both flexibility and performance. The project is entirely open source and free to use, making enterprise-grade inference accessible to teams of any size.