Trainium3: AWS 3nm AI Training Chip Arrives Late 2025

Trainium3: AWS’s 3nm AI Chip Arriving Late 2025

AWS is betting big on custom silicon. At re:Invent 2024, the company announced Trainium3—its first 3-nanometer AI chip that promises to reshape how enterprises train large language models. Here’s what you need to know.

Trainium Evolution

Why Custom AI Chips Matter

NVIDIA GPUs dominate AI training, but they’re expensive and in short supply. AWS’s custom chips offer:

• 30-40% better price-performance than GPU instances
• No supply constraints—AWS controls production
• Optimized for AWS—native integration with SageMaker
• Energy efficiency—4x better than previous generation

Trn2 UltraServers: 64-Chip Clusters

The new Trn2 UltraServers connect four Trn2 instances using NeuronLink, creating a single 64-chip system for massive model training:

# Launch Trn2 instance for AI training
aws ec2 run-instances \
    --instance-type trn2.48xlarge \
    --image-id ami-xxxxxxxxx \
    --key-name my-key \
    --subnet-id subnet-xxxxxxxx

# Check Neuron devices
neuron-ls

Training Models on Trainium

import torch
import torch_neuronx

# Compile model for Trainium
model = MyTransformerModel()
example_input = torch.randn(1, 512)

# Trace and compile for Neuron
traced_model = torch_neuronx.trace(model, example_input)

# Save compiled model
traced_model.save('model_neuron.pt')

# Run inference
output = traced_model(example_input)

2025-2026 Roadmap

For organizations training large models, Trainium offers a compelling alternative to fighting for NVIDIA GPU capacity. With Trainium3 arriving later this year, the price-performance gap will only widen.