I'm building real-time inference endpoints and need low-latency GPU serving

For real-time inference endpoints with low latency on GPU, the main goal is to keep the model “hot,” minimize framework overhead, and maximize batching/throughput without hurting tail latency.

What to optimize

• Cold start time: keep instances warm, avoid frequent autoscaling to zero.
• Model loading time: preload weights, use optimized formats.
• GPU utilization: avoid underutilized GPUs, but don’t overbatch if p95 latency matters.
• Network overhead: keep clients and serving close; prefer region-local traffic.
• Framework overhead: choose a lightweight serving stack.
• Memory fragmentation / KV cache: especially important for LLMs.

Good serving stacks for low-latency GPU inference

• TensorRT / TensorRT-LLM: best for maximum performance if your model is supported and you can invest in optimization.
• NVIDIA Triton Inference Server: strong general-purpose production server, supports dynamic batching, concurrent model execution, and multiple backends.
• vLLM: excellent for LLM serving with paged attention and high throughput/latency balance.
• Hugging Face TGI: simpler LLM serving, good production ergonomics.
• TorchServe / custom FastAPI + PyTorch: easiest to start, but usually not the best latency at scale.

Practical latency tips

• Use FP16/BF16 or INT8/FP8 where acceptable.
• Enable CUDA graphs if your input shapes are stable.
• Use dynamic batching only if it improves throughput without violating p95/p99 targets.
• Pin one model per GPU when latency is critical.
• Warm up the model with representative requests.
• Keep request preprocessing lightweight and on CPU if it doesn’t bottleneck.
• Use gRPC for lower overhead than REST in many cases.
• Co-locate serving and feature retrieval if you have online features.

If you’re serving LLMs specifically

• Prefer vLLM or TensorRT-LLM.
• Use:
  • continuous batching
  • KV cache optimization
  • streaming responses
  • token-level latency monitoring
• Watch out for:
  • long prompts
  • context-length spikes
  • queue buildup
  • GPU memory fragmentation

Deployment patterns

• Single-model dedicated endpoint for strict latency SLAs.
• Multi-model shared GPU only if you can tolerate some jitter.
• Autoscale based on queue depth, GPU utilization, and p95 latency, not CPU alone.
• Use rolling deploys and shadow traffic to avoid downtime.

Observability metrics

Track:

• p50 / p95 / p99 latency
• queue time vs compute time
• GPU utilization
• GPU memory usage
• token throughput
• error rate / timeouts
• cold starts

If you tell me:

1. the model type (vision, tabular, LLM, embedding, etc.),
2. your latency target,
3. expected QPS, and
4. your GPU type,

I can recommend a concrete serving architecture and stack.