What should I use for low-latency model serving?

For low-latency model serving, the best choice depends on the model type and traffic pattern, but here’s the practical short list:

Best general-purpose options

• NVIDIA Triton Inference Server
  Great for GPU-backed inference, batching, multi-framework support, and high-throughput/low-latency serving.
• TorchServe
  Good if you’re mainly serving PyTorch models and want an easier setup, though it’s less flexible/performance-oriented than Triton.
• BentoML
  Strong for packaging and deploying models quickly with production-friendly APIs; good for simpler deployments.

If you’re serving LLMs

• vLLM
  Often the best default for low-latency LLM serving due to paged attention and efficient batching.
• Hugging Face TGI (Text Generation Inference)
  Solid production option for transformer-based text generation.
• TensorRT-LLM
  Best when you want maximum NVIDIA GPU performance and can invest in optimization.

If you need very low tail latency

• Use ONNX Runtime or TensorRT for optimized execution.
• Consider gRPC, preloading models, warm instances, and dynamic batching carefully, since batching can improve throughput but hurt p99 latency if misconfigured.

Rule of thumb

• LLMs: start with vLLM
• General GPU inference: start with Triton
• Simple PyTorch deployment: TorchServe or BentoML
• NVIDIA-heavy optimized deployment: TensorRT / TensorRT-LLM

If you tell me:

1. model type (LLM, vision, tabular, etc.),
2. CPU vs GPU,
3. target latency,
4. expected QPS,

…I can recommend a more precise stack.