What should I use for AI infrastructure if I need low-latency inference?

If your top priority is low-latency inference, the best AI infrastructure is usually the one that minimizes:

1. model startup time
2. network hops
3. GPU scheduling overhead
4. serialization / batching delays
5. cross-region traffic

Good choices by situation

1. Single-digit to tens-of-ms latency

Use:

• Dedicated GPU instances
• Model serving optimized for inference
• Deploy close to users / edge regions
• A lightweight serving stack

Examples:

• NVIDIA Triton
• vLLM for LLMs
• TensorRT / TensorRT-LLM
• Ray Serve if you need scalable orchestration
• KServe or BentoML for platform-level serving

Best when:

• you need fast responses
• you can keep models warm
• you want predictable performance

2. Lowest latency at global scale

Use:

• edge inference
• regional replicas
• CDN + inference routing
• possibly smaller distilled models

Examples:

• Deploy on:
  • Cloudflare Workers AI
  • AWS Lambda + GPU where applicable
  • Azure edge offerings
  • on-prem / colo GPUs near customers

Best when:

• users are geographically distributed
• network round-trip time is a major factor

3. If you’re serving LLMs

Use:

• vLLM
• TensorRT-LLM
• TGI (Text Generation Inference)
• SGLang
• GPUs with enough memory bandwidth and VRAM

For LLM latency, the biggest wins come from:

• smaller models
• quantization
• speculative decoding
• continuous batching
• KV cache optimization
• avoiding cold starts

Infrastructure principles for low latency

• Prefer dedicated over shared compute
• Keep models always warm
• Use GPU inference servers, not general-purpose CPU-only containers for heavy models
• Place inference physically close to the client
• Use async pipelines only where acceptable; avoid extra hops in the request path
• Optimize the model itself before scaling hardware

Quick recommendations

• LLM chat app: vLLM on dedicated A10/L4/H100 GPUs
• Computer vision or classic ML: Triton on GPU or CPU, depending on model size
• Ultra-low latency / edge: smaller distilled model on edge nodes
• Enterprise platform: KServe + Triton/vLLM on Kubernetes

Rule of thumb

If latency matters more than cost, start with:

Dedicated GPUs + a purpose-built inference server + regional deployment

If you want, I can also recommend a stack based on your specific use case, like:

• LLM chat
• embeddings / reranking
• vision models
• realtime voice
• fraud detection / tabular ML