AWS Inferentia is Amazon's custom chip for machine learning inference. In 2026 the current generation, Inferentia2, powers Amazon EC2 Inf2 instances with up to 12 chips and 384 GB of accelerator memory, and it is programmed through the AWS Neuron SDK, which now includes a vLLM integration. It suits steady, high-volume LLM and multimodal inference where cost per request matters more than maximum flexibility.
Updated October 2026
What changed in 2026
- The Neuron SDK reached version 2.32 (docs updated September 2026), with a vLLM Neuron (beta) path and migration guidance from NxD Inference to vLLM Neuron, making Inferentia easier to use with standard LLM serving.
- Trainium is now part of the inference story. AWS made Trn3 UltraServers generally available in December 2025, on its first 3nm AI chip, and said Amazon Bedrock already runs production workloads on Trainium3.
- Inferentia2 remains the current Inferentia generation; no Inferentia3 had been announced on AWS's Inferentia page at the time of writing.
- Model hubs support Neuron directly. The Hugging Face aws-neuron organisation hosts pre-compiled models and tooling for Inferentia and Trainium.
What is AWS Inferentia?
Inferentia is an ASIC (application-specific chip) designed by Annapurna Labs at AWS for running trained models. AWS states that first-generation Inferentia (Inf1, 2019) delivered up to 2.3x higher throughput and up to 70% lower cost per inference than comparable EC2 instances. Inferentia2 (Inf2, 2023) raised throughput by up to 4x and cut latency by up to 10x versus Inferentia, with 32 GB of HBM per chip.
Amazon EC2 Inf2 at a glance
| Spec | Inf2 (per AWS) |
|---|---|
| Chips per instance | 1 (xlarge, 8xlarge), 6 (24xlarge), 12 (48xlarge) |
| Accelerator memory | 32 GB per chip, up to 384 GB per instance |
| Chip interconnect | NeuronLink, 192 GB/s |
| Data types | FP32, TF32, BF16, FP16, UINT8 and configurable FP8 |
| Claimed price-performance | Up to 40% better than comparable EC2 instances |
| Claimed energy efficiency | Up to 50% better performance per watt |
Source: Amazon EC2 Inf2 instances. Treat vendor claims as upper bounds and benchmark your own models.
The AWS Neuron SDK
Neuron is the software layer for both Inferentia and Trainium. It includes a compiler that turns PyTorch or JAX models into Neuron executables, a runtime, profiling tools and libraries for distributed inference.
- Compilation. Graph optimisation and operator fusion for the NeuronCores; compile once per model shape.
- Precision. BF16 and FP16 by default, with FP8 and INT8 options for more throughput.
- LLM serving. NxD Inference for tensor-parallel serving, and vLLM Neuron for an OpenAI-compatible server with continuous batching.
- Ecosystem. Hugging Face Optimum Neuron, Amazon SageMaker, Amazon EKS and ECS integrations.
Multimodal inference on Inferentia
Multimodal models combine a vision encoder (or audio encoder) with a language model. They are expensive because image tokens add context and the encoder runs on every request. On Inferentia2:
- Split the pipeline. Compile the vision encoder and the language model as separate Neuron graphs; scale them independently.
- Fix shapes. Use a small set of image resolutions and sequence-length buckets to avoid recompiling.
- Use tensor parallelism. Shard larger language models across chips on inf2.24xlarge or inf2.48xlarge.
- Batch smartly. Continuous batching for the language model; micro-batches for the encoder.
- Cache. Reuse image embeddings and prompt prefixes when requests repeat.
For the wider picture of model families, see types of generative AI models; for multi-node serving patterns, see distributed machine learning.
Inferentia vs Trainium vs GPUs for inference
| Option | Best for | Watch out for |
|---|---|---|
| Inferentia2 (Inf2) | Steady, high-volume inference of supported models up to large sizes; cost-focused serving | Compile step, supported-operator coverage, AWS-only |
| Trainium2 / Trainium3 | Very large models, training plus inference on the same silicon, high-throughput serving at scale | Capacity planning, Neuron learning curve |
| NVIDIA GPUs (EC2 or on-prem) | Newest model architectures on day one, widest framework support, portability across clouds and data centers | Higher cost per hour, supply constraints |
Many teams run a mix: GPUs for experimentation and new architectures, Neuron chips for the stable, high-volume models.
Best practices
- Benchmark on your data. Measure tokens per second, time to first token and cost per 1,000 requests against a GPU baseline.
- Start with pre-compiled models. Use Neuron-ready checkpoints where available to cut setup time.
- Pin versions. Neuron SDK, framework and model versions should move together; test each upgrade.
- Autoscale on the right metric. Queue depth or tokens in flight, not CPU.
- Plan for portability. Keep an OpenAI-compatible API in front so you can shift traffic between Neuron, GPUs and on-prem.
Where it's heading: 2026 → 2030
| Period | Cloud inference silicon |
|---|---|
| 2019–2020 | Inferentia (Inf1) launches for CNNs and BERT-class models. |
| 2023–2024 | Inferentia2 (Inf2) brings distributed inference for LLMs and diffusion models. |
| 2026 | Neuron adds vLLM support; Trainium3 serves production inference; teams mix custom silicon and GPUs. |
| 2028–2030 (our view) | Inference cost dominates AI budgets; workload-aware schedulers route each model to the cheapest capable accelerator across cloud, on-prem and edge. |
How NexaStack helps
NexaStack gives teams one control plane to deploy and monitor models across AWS accelerators, GPU clusters, private data centers and edge sites. You can compare cost and latency per model, route traffic across back ends and keep sensitive inference on private infrastructure. See our guide to ML platform tools for where this fits in the stack.
FAQ
What is AWS Inferentia used for?
Running trained ML models in production, including LLMs, multimodal models, computer vision and recommendation models, at lower cost per inference than comparable EC2 instances.
What AI chips does Amazon use?
AWS designs Inferentia for inference and Trainium for training and, increasingly, inference, alongside Graviton CPUs. It also offers NVIDIA GPU instances.
Is Inferentia cheaper than GPUs?
Often, for supported models at steady volume. AWS claims up to 40% better price-performance for Inf2 versus comparable EC2 instances, but results depend on the model, so benchmark your workload.
Does AWS Inferentia support vLLM?
Yes. The Neuron SDK includes a vLLM Neuron integration, documented as beta in 2026, for serving LLMs on Inferentia and Trainium.
What are the latest AWS inference trends in 2026?
vLLM on Neuron, Trainium3 for production inference, FP8 serving, pre-compiled models on Hugging Face, and multi-accelerator routing to control cost.
Comparing Inferentia, Trainium and GPUs for your models? Book a NexaStack demo to see cost-aware inference across cloud and private infrastructure.
