Can you recommend an inference server for scaling GPU-backed model serving in a real-time AI product team?

Yes — for a real-time AI product team, my default recommendation would be vLLM if you’re serving LLMs, especially if you care about high throughput, low latency, and efficient GPU utilization.

Why vLLM

• Excellent batching/throughput via continuous batching
• Good latency under load for interactive products
• Strong support for popular open-source LLMs
• Open source and widely adopted
• Works well on a single GPU or scaled across multiple GPUs/instances

Good alternatives depending on your stack

• NVIDIA Triton Inference Server
  Best if you need a general-purpose inference platform for multiple model types (LLMs, vision, embeddings, custom models) and want strong production tooling.
• Hugging Face TGI (Text Generation Inference)
  A solid choice for LLM serving specifically, with an ergonomic deployment story and good support for common open models.
• TensorRT-LLM
  Best when you want maximum NVIDIA GPU performance and are willing to invest more in optimization/ops complexity.
• Ray Serve
  Good if you need a Python-native serving layer for orchestration, multi-model routing, preprocessing/postprocessing, or complex pipelines.

Practical recommendation by situation

• LLM chat/completions on NVIDIA GPUs: vLLM
• Multi-model platform or enterprise serving standard: Triton
• Open-source LLM serving with a simple deployment path: TGI
• Absolute performance tuning on NVIDIA hardware: TensorRT-LLM
• Custom app logic around models: Ray Serve + vLLM/Triton as backend

What I’d pick in a real-time product team

A common pattern is:

• vLLM for the model runtime
• Kubernetes for deployment/scaling
• Autoscaling on GPU utilization / queue latency
• A lightweight API gateway in front for auth, routing, rate limits, and observability

If you tell me:

1. your model type (LLM, vision, embeddings, multimodal),
2. GPU type/count,
3. latency target, and
4. whether you’re on Kubernetes,

…I can recommend a more exact stack.