vLLM

vLLM — Technical Use Cases

1. High-Throughput Inference API Server

Deploy vLLM as an OpenAI-compatible or gRPC API server to serve LLM requests at scale. PagedAttention and continuous batching maximize tokens-per-second across concurrent requests, making it suitable for production traffic with strict SLA requirements.

2. Multi-Tenant LoRA Serving

Use vLLM's multi-LoRA support to serve multiple fine-tuned adapters from a single loaded base model, reducing GPU memory overhead and operational complexity in multi-tenant or multi-task environments.

3. Distributed Multi-GPU / Multi-Node Inference

Leverage tensor, pipeline, data, expert, or context parallelism to run models that exceed single-GPU memory capacity, or to horizontally scale throughput across a cluster managed by Kubernetes, Ray Serve, or KubeRay.

4. Quantized Model Deployment

Deploy memory-efficient quantized models (FP8, INT4, GPTQ, AWQ, GGUF) on cost-constrained hardware without sacrificing API compatibility, enabling inference on smaller GPU instances or even CPUs.

5. Speculative Decoding Pipelines

Integrate n-gram, suffix, or EAGLE speculative decoding to reduce per-token latency for latency-sensitive applications such as real-time coding assistants or interactive chat (e.g., Claude Code, Codex integrations).

6. LLM Application Backend

Use vLLM as the inference backend for LangChain, LlamaIndex, Haystack, AutoGen, or Dify pipelines — providing a self-hosted, cost-controlled alternative to third-party API providers with a compatible interface.