Infrastructure
Definition
AI infrastructure covers hardware (GPUs, TPUs, custom accelerators) and software (distributed training, serving, orchestration) for training and deploying large models.
Scale is driven by LLMs and large vision models; training may use thousands of GPUs; serving uses model compression (e.g. quantization) and batching to meet latency and cost. Frameworks (PyTorch, JAX, TensorFlow) provide the programming model; clouds and on-prem clusters provide the hardware and orchestration.
How it works
Data and config (model, hyperparameters) feed into train: distributed training runs across many devices using data parallelism (replicate model, split data) and/or model parallelism (split model across devices). Frameworks (PyTorch, JAX) and orchestrators (SLURM, Kubernetes, cloud jobs) manage scheduling and communication. The trained model is then served: loaded on inference hardware, optionally quantized, and exposed via an API. Serving uses batching, replication, and load balancing to meet throughput and latency; monitoring and versioning are part of the pipeline.
Use cases
ML infrastructure covers training at scale and serving with the right latency, throughput, and reliability.
- Distributed training of large models across GPU/TPU clusters
- Serving models at scale with batching and replication
- End-to-end ML pipelines from data to deployment