AI Infrastructure and Inference Essentials | Generated by AI
Question: What does AI infra mean? What about inference / scheduler? What does it involve?
Answer:
AI infra (short for AI infrastructure) refers to the specialized combination of hardware, software, networking, and related systems built to support the full lifecycle of artificial intelligence workloads. This includes developing, training, fine-tuning, and especially deploying/running AI models (particularly large language models and generative AI in recent years).
It differs from traditional IT infrastructure because AI workloads are extremely compute-intensive, memory-hungry, and often involve massive parallel processing, huge datasets, and low-latency requirements.
Main phases AI infra supports
- Training — building/fine-tuning models (very GPU-heavy, long-running jobs)
- Inference — running a trained model to generate predictions, answers, images, etc. for users (the dominant cost for most deployed AI products today)
What “inference” means in this context
Inference is the “serving” or “production” phase where a pre-trained model takes new input (a prompt, an image, etc.) and produces output.
For large models like GPT, Llama, Claude, etc.:
- It is autoregressive (generates one token at a time)
- It is memory-intensive (especially due to the KV cache that grows with context length)
- It needs to be fast (low latency for chat/apps) and cheap (high throughput to serve many users)
Inference usually consumes far more total compute dollars than training once a model is widely deployed.
What an “inference scheduler” (or just “scheduler”) is
A scheduler in AI infra (especially for inference) is the software component that intelligently decides:
- Which requests (user prompts) to process next
- How to batch multiple requests together to maximize GPU utilization
- How to allocate GPU memory and compute across concurrent users
- When to preempt, prioritize, or evict requests
- How to route requests to the best GPU/node (considering load, prefix caching, etc.)
Popular examples in 2025–2026:
- vLLM scheduler (continuous batching, PagedAttention)
- TensorRT-LLM scheduler
- Run:AI scheduler (enterprise/multi-tenant fairness)
- Custom intelligent inference schedulers (prefix-cache aware routing, load balancing)
Good schedulers dramatically improve throughput (tokens/second), reduce tail latency, and lower cost per million tokens.
What AI infra typically involves (key components)
- Compute — mostly NVIDIA GPUs (H100, H200, B200, Blackwell), sometimes AMD Instinct, Google TPUs, or custom silicon
- High-speed networking — InfiniBand or ultra-low-latency Ethernet for connecting thousands of GPUs
- Storage — fast NVMe, distributed file systems (e.g., for checkpoints, datasets), object storage for massive training data
- Orchestration & scheduling — Kubernetes + operators, Slurm (HPC style), or specialized platforms like Run:AI, KubeRay, or cloud-native schedulers
- Inference engines & serving frameworks — vLLM, TGI (Text Generation Inference), TensorRT-LLM, SGLang, LMDeploy, etc.
- Model optimization — quantization (4-bit, 8-bit), pruning, distillation, speculative decoding
- Monitoring & observability — GPU utilization, queue depth, latency histograms, cost tracking
- MLOps / platform layer — experiment tracking, model registry, CI/CD for models, autoscaling
In short: “AI infra” today is mostly about building efficient, scalable, cost-effective systems to run inference at scale — and schedulers are one of the most important pieces that determine whether your GPU cluster feels 2× or 5× more efficient.
References:
- What is AI Infrastructure? - IBM
- AI Infrastructure: Key Components - Cloudian
- Life of an inference request (vLLM)
- LLM Inference Scheduling Overview
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[What Is AI Infrastructure? NVIDIA Glossary](https://www.nvidia.com/en-us/glossary/ai-infrastructure)