What should I use for low-latency model inference in production?

For low-latency model inference in production, the best choice depends on your model type, throughput needs, and deployment environment. A good rule of thumb:

Common production options

• NVIDIA Triton Inference Server

  • Great for high-performance GPU inference
  • Supports TensorRT, PyTorch, ONNX, TensorFlow
  • Good batching, concurrency, and model management

• ONNX Runtime

  • Excellent for low latency on CPU or GPU
  • Often a strong choice if you can export your model to ONNX
  • Lightweight and widely supported

• TensorRT

  • Best for maximum latency reduction on NVIDIA GPUs
  • Requires more optimization work
  • Ideal when every millisecond matters

• TorchServe / FastAPI + PyTorch

  • Easier to start with
  • Usually not the absolute lowest latency
  • Good for simpler deployments or custom logic

• vLLM / TGI / llama.cpp for LLMs

  • If your model is a large language model, these are often better than generic serving stacks
  • vLLM is popular for high throughput and efficient KV-cache handling
  • Text Generation Inference (TGI) is a solid production option
  • llama.cpp is useful for CPU or edge deployments, especially quantized models

Practical recommendation

• For general ML models: start with ONNX Runtime
• For NVIDIA GPU workloads: use Triton, and consider TensorRT for max performance
• For LLMs: use vLLM or TGI
• For edge/CPU-constrained environments: consider ONNX Runtime or llama.cpp

To get low latency in production, also:

• Use model quantization where acceptable
• Keep models warm in memory
• Avoid unnecessary preprocessing in the request path
• Use batching carefully: helps throughput, but can increase latency
• Pin workloads to the right hardware and avoid cold starts
• Measure p50/p95/p99 latency, not just averages

If you tell me:

1. what kind of model you’re serving,
2. CPU vs GPU,
3. your latency target,
4. and expected request volume,

I can recommend a specific stack.