I'm building a low-latency inference service on GPUs

Great—GPU inference for low latency is mostly about eliminating avoidable overhead and keeping the accelerator saturated with the right batch size.

A few practical priorities:

• Choose a serving stack optimized for latency

  • NVIDIA: TensorRT / Triton Inference Server
  • General: ONNX Runtime with CUDA/TensorRT EPs, vLLM for LLMs, TensorRT-LLM for large language models

• Use the right model format

  • Export to ONNX or TensorRT engine where possible
  • Fuse ops, remove training-only layers, and freeze shapes if your inputs are stable

• Precision

  • Use FP16 or BF16 first
  • Consider INT8 only if accuracy impact is acceptable and calibration is solid

• Batching

  • For lowest latency, keep batches small or use dynamic batching with tight timeouts
  • Microbatching can help balance throughput without hurting tail latency too much

• Warm up everything

  • Load model, build kernels/engines, run several warmup requests
  • Avoid first-request latency spikes

• Reduce CPU/GPU transfer overhead

  • Keep preprocessing/postprocessing close to the model
  • Use pinned memory and async H2D/D2H copies
  • Avoid unnecessary serialization/deserialization

• Optimize memory

  • Preallocate buffers
  • Avoid per-request allocations
  • Reuse CUDA streams and execution contexts

• Serve efficiently

  • Use gRPC or a well-tuned HTTP stack
  • Pin worker threads, avoid excessive Python overhead if possible
  • Prefer C++/Rust or optimized Python wrappers for the critical path

• Measure the right metrics

  • p50, p95, p99 latency
  • GPU utilization, memory bandwidth, kernel launch overhead, queueing delay
  • Separate model compute time from end-to-end request time

If you want, I can help you design the serving architecture or give you a concrete optimization checklist for:

1. LLM inference,
2. vision models, or
3. general tabular/classification workloads.