AI Infrastructure

vLLM

vLLM is a high-throughput, memory-efficient inference and serving engine for large language models (LLMs). It’s open-source and designed to make LLM inference faster and more cost-effective by maximizing GPU utilization and throughput, using techniques like PagedAttention and dynamic batching to reduce memory overhead and serve many requests on the same hardware. Developed originally in an academic setting and now maintained by a community of contributors, vLLM is used at scale to serve LLM inference workloads efficiently without relying on proprietary cloud inference APIs.

4/5
Pricing: Free to use and deployVisit Website
GPU server racks running high-throughput LLM inference with vLLM, illustrating efficient large language model serving on modern AI infrastructure

Pros

  • Significantly improves GPU utilization for LLM inference
  • Reduces memory fragmentation through PagedAttention
  • Designed specifically for high-throughput, multi-request serving
  • Open-source with no licensing fees
  • Actively adopted in real production environments
  • Strong fit for on-prem and hybrid AI infrastructure

Cons

  • Requires GPU infrastructure to be useful
  • Not beginner-friendly compared to managed AI APIs
  • Limited value for low-traffic or single-user inference
  • Operational complexity increases at scale
  • Requires Linux and CUDA-capable hardware

Best Use Cases

  • •High-volume LLM inference servers
  • •Self-hosted enterprise AI deployments
  • •Cost-optimized AI inference pipelines
  • •On-prem or hybrid AI infrastructure
  • •Research labs serving models to multiple users
  • •Internal developer platforms running private models

vLLM is an open-source inference engine designed to address one of the most expensive and overlooked problems in modern AI systems: efficient model serving.

As organizations move from experimentation to production, inference throughput and GPU utilization become far more important than raw model size. vLLM focuses on this layer of the stack by optimizing how large language models are loaded, scheduled, and served across shared GPU resources.

Its core innovation, PagedAttention, reduces memory waste and allows multiple inference requests to coexist on the same GPU without the fragmentation issues common in traditional serving stacks. This enables higher request density, better throughput, and lower operational cost per token.

Unlike managed AI APIs, vLLM is designed for teams that want full control over their models and infrastructure. It integrates well with modern Linux-based AI stacks and is increasingly used in production environments where inference efficiency directly impacts cost and scalability.

vLLM is not intended for casual users or small projects. Its value becomes clear in environments where GPU resources are shared across many requests and where inference cost optimization is a priority.

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