AI Infrastructure
infrastructure
What Is Latency in AI?
AsterMind Team
Latency in AI refers to the time delay between providing input to an AI system and receiving its output. In the context of LLMs, latency is typically measured as Time to First Token (TTFT) — how quickly the model begins generating a response — and tokens per second — how fast subsequent tokens are produced.
Why Latency Matters
- User Experience — Users expect near-instant responses; delays above 2 seconds feel sluggish
- Real-Time Applications — Autonomous driving, fraud detection, and trading require millisecond responses
- Conversational AI — Chatbots with high latency feel unnatural and frustrating
- Throughput — Lower latency per request means higher system throughput
- Cost — Faster inference reduces compute costs per request
Components of AI Latency
| Component | Description | Typical Duration |
|---|---|---|
| Network Latency | Round-trip time between client and server | 10-200ms |
| Preprocessing | Tokenization, embedding, data preparation | 1-50ms |
| Queue Wait | Time waiting for available compute resources | 0-5000ms |
| Inference (TTFT) | Model processes input and generates first token | 100-2000ms |
| Generation | Producing remaining output tokens | Varies by length |
| Postprocessing | Formatting, safety filtering, response assembly | 1-20ms |
Latency Metrics for LLMs
- Time to First Token (TTFT) — How quickly the model starts responding
- Tokens Per Second (TPS) — Speed of subsequent token generation
- End-to-End Latency — Total time from request to complete response
- P50/P99 Latency — Median and 99th percentile response times
Optimization Techniques
- Model Quantization — Reduce precision to speed up computation
- Model Distillation — Use smaller models for faster inference
- KV-Cache — Cache key-value pairs for conversational context
- Speculative Decoding — Use a fast draft model verified by the main model
- Batching — Process multiple requests together for GPU efficiency
- Edge Deployment — Run models closer to users to eliminate network latency
- Hardware Acceleration — GPUs, TPUs, or specialized AI chips
ELMs: Ultra-Low Latency AI
AsterMind's Extreme Learning Machines achieve sub-millisecond inference for classification tasks. With no iterative computation, GPU dependency, or network round-trip, ELMs deliver the lowest possible latency for real-time AI applications at the edge.