Beginner

Introduction to AWS Inferentia & Trainium

Discover AWS custom-designed machine learning chips — Inferentia for inference and Trainium for training — and understand how they deliver higher performance at lower cost than traditional GPUs.

What Are Inferentia and Trainium?

AWS Inferentia and AWS Trainium are purpose-built machine learning accelerators designed by AWS's Annapurna Labs. Unlike general-purpose GPUs, these chips are optimized specifically for ML workloads, delivering better price-performance for inference and training tasks.

Inferentia focuses on high-throughput, low-latency inference, while Trainium is designed for cost-effective model training at scale. Together, they provide a complete custom silicon solution for the ML lifecycle on AWS.

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Good to know: AWS Inferentia was launched in 2019 (Inf1), followed by Inferentia2 (Inf2) in 2023. Trainium (Trn1) launched in 2022, with Trn2 following in 2024. These chips represent AWS's strategy to reduce dependency on NVIDIA GPUs and offer customers more cost-effective ML compute options.

Why Custom Silicon for ML?

General-purpose GPUs are powerful but come with overhead for features ML workloads don't need. Custom chips eliminate this overhead:

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Cost Savings

Up to 50% lower cost per inference compared to GPU-based instances, with predictable pricing models.

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Higher Throughput

Purpose-built NeuronCores deliver higher throughput for common ML operations like matrix multiplication and attention.

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Lower Latency

Optimized data paths and dedicated ML engines reduce latency for real-time inference applications.

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Energy Efficiency

Custom silicon consumes less power per inference, reducing both costs and environmental impact.

Inferentia vs Trainium

FeatureInferentia (Inf2)Trainium (Trn1)
Primary useModel inferenceModel training
NeuronCores per chip22
HBM per chip32 GB32 GB
Chips per instance (max)12 (inf2.48xlarge)16 (trn1.32xlarge)
NeuronLink interconnect✓✓
Best forLLM inference, vision, NLPLLM training, fine-tuning

When to Use Custom Silicon

  • Cost-sensitive inference: When you need to serve models at scale and cost is a primary concern
  • Supported model architectures: Transformer-based models (BERT, GPT, T5, Llama, Stable Diffusion)
  • High-throughput serving: Applications requiring thousands of inferences per second
  • Large model training: When training large language models and you want alternatives to expensive GPU clusters
  • AWS-native workloads: When your infrastructure is already on AWS and you want tight integration

When to Stick with GPUs

  • Custom CUDA kernels: Workloads that rely on custom CUDA code or CUDA-specific libraries
  • Unsupported operations: Models using operators not yet supported by the Neuron compiler
  • Rapid prototyping: When you need maximum framework flexibility during experimentation
  • Multi-cloud requirements: When portability across cloud providers is a priority
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Key takeaway: AWS Inferentia and Trainium offer compelling cost-performance advantages for supported ML workloads. The Neuron SDK makes it straightforward to compile and run models on these chips with minimal code changes. Start with Inferentia for inference workloads where cost savings can be immediate and significant.