AWS Inferentia2 vs NVIDIA T4
AWS custom inference chip vs NVIDIA's budget GPU
Inferentia2 is AWS's custom inference accelerator with 32GB of memory. The T4 is NVIDIA's widely available budget inference GPU with 16GB. Both target cost-optimised inference.
Pricing Comparison
Specifications
| Specification | AWS Inferentia2 | NVIDIA T4 |
|---|---|---|
| Manufacturer | AWS | NVIDIA |
| Architecture | Inferentia2 | Turing |
| Accelerator Type | GPU | GPU |
| Primary Use | inference | inference |
| Memory (VRAM) | 32 GB | 16 GB |
| FP16 Performance | — | 65 TFLOPS |
| TDP | — | 70W |
Detailed Analysis
Inferentia2 and the T4 both target cost-sensitive inference deployments but through different approaches. Inferentia2 is a purpose-built inference chip designed by AWS, while the T4 is NVIDIA's general-purpose inference GPU.
Inferentia2 doubles the memory of its predecessor to 32GB, enabling it to serve larger models than the T4's 16GB allows. For models in the 7B-15B parameter range with quantisation, Inferentia2 can be the more capable option.
The T4's advantage is universality. It's available across all major cloud providers and regions, runs standard CUDA/TensorRT, and has years of production-proven reliability. Inferentia2 is AWS-only and requires the Neuron SDK.
For pure inference throughput per dollar on AWS, Inferentia2 is designed to win. For portability, ecosystem compatibility, and availability outside AWS, the T4 is the safer choice.
Verdict
Neither is designed for training.
Inferentia2 for cost-optimised inference on AWS. T4 for multi-cloud and CUDA compatibility.
Inferentia2 on AWS for supported models. T4 everywhere else.
Frequently Asked Questions
Should I use Inferentia2 or T4 for inference on AWS?
If your model is supported by the Neuron SDK and you're committed to AWS, Inferentia2 can deliver better price/performance. If you need CUDA compatibility or may move to other clouds, the T4 is safer.
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