AWS Inferentia: Optimizing Multimodal Model Inference Costs

Artificial intelligence (AI) and Machine Learning (ML) have revolutionized numerous industries, driving innovations in natural language processing (NLP), computer vision, and multimodal applications that combine various data types such as text, images, and audio. However, deploying these models at scale presents a major challenge—the high cost of inference. AWS Inferentia, Amazon Web Services' custom-built inference chip, is designed to reduce these costs while maintaining high performance. This blog explores how AWS Inferentia optimizes multimodal model inference costs, helping businesses achieve scalability and efficiency. 

What is AWS Inferentia? 

AWS Inferentia is a specialized AI inference chip developed by AWS to handle deep learning workloads efficiently. It is used in Amazon EC2 Inf1 instances, which are optimized for high throughput and low-latency inference. 

Fig 1.1. AWS Inferentia 

Core Capabilities and Advantages of AWS Inferentia 

• High Compute Performance: Inf2 instances, powered by up to 12 AWS Inferentia2 chips, offer up to 2.3 petaflops of compute, providing 4x higher throughput and 10x lower latency than Inf1 instances. 

• Large High-Bandwidth Memory: Each Inferentia2 chip includes 32 GB of high-bandwidth memory (HBM), enabling up to 384 GB of shared accelerator memory with a total 9.8 TB/s memory bandwidth, which is 10x faster than first-generation Inferentia. 

• NeuronLink Interconnect: Inf2 instances utilize 192 GB/s of NeuronLink, a high-speed, nonblocking interconnect for efficient data transfer between Inferentia2 chips. This enables direct communication between chips without CPU intervention, improving throughput and reducing latency.

• Support for Multiple Data Types: Inferentia2 supports a wide range of data types, including FP32, TF32, BF16, FP16, UINT8, and the new configurable FP8 (cFP8). The AWS Neuron SDK can autocast high-precision FP32 and FP16 models to lower-precision data types, optimizing performance while maintaining accuracy.

• State-of-the-Art Deep Learning Optimizations: Inf2 instances incorporate advanced optimizations such as dynamic input shape support, custom operators and stochastic rounding for improved accuracy and performance.

• Scalability and Parallel Processing: Inferentia chips are designed to handle large-scale deep learning models efficiently, making them ideal for multimodal applications that require fast inference speeds and optimized resource utilization.

The Importance of Multimodal Model Inference Optimization 

What Are Multimodal Models? 

Multimodal models process multiple data types (e.g., text, images, and audio) to generate more sophisticated AI outputs. Popular examples include: 

• CLIP (Contrastive Language–Image Pretraining): Trained on both text and images for zero-shot learning. 

• DALL·E: Generates images from textual descriptions. 

• Whisper: OpenAI's automatic speech recognition (ASR) model. 

• GPT-4V (Vision): A version of GPT-4 that processes both images and text. 

The Cost Challenge of Multimodal Model Inference 

Multimodal models require substantial computational resources because they often involve large transformers, CNNs (Convolutional Neural Networks), and attention mechanisms. Running inference on GPUs can be expensive due to high energy consumption and memory bandwidth requirements. Inferentia offers a cost-effective alternative. 

Achieving Cost Efficiency with AWS Inferentia 

Fig 1.2. Cost Optimization with AWS Inferentia

The image showcases five key benefits of AWS Inferentia for multimodal AI inference, illustrated with a human and a robot shaking hands. The numbered points highlight lower inference costs, higher throughput, dynamic batching, model compilation with AWS NeurON SDK, and improved energy efficiency. 

Lower Cost Per Inference 

AWS Inferentia is designed for cost efficiency. It is built specifically to handle inference workloads more efficiently than general-purpose GPUs. By optimizing hardware and software for inference tasks, Inferentia achieves up to 70% lower cost per inference compared to traditional GPUs. This means businesses can scale their AI applications without incurring excessive infrastructure costs. Lowering the cost per inference is critical for organizations deploying large-scale AI services, such as recommendation engines, chatbots, and autonomous systems. 

Energy Efficiency 

Inference workloads often run 24/7, consuming significant amounts of power, especially when handled by GPUs designed primarily for training. Inferentia chips are optimized for power efficiency, meaning they use less energy while still delivering high-performance inference. This translates to lower electricity costs and reduced carbon footprint, making it a more sustainable option for AI-driven businesses. By consuming less power, organizations can achieve cost savings while meeting sustainability goals. 

Higher Throughput and Low Latency 

AWS Inferentia is engineered to support high-throughput, low-latency inference. It enables multiple inference requests to be processed simultaneously, ensuring that large-scale AI applications can operate efficiently. This is particularly beneficial for real-time applications such as fraud detection, voice assistants, and autonomous driving, where minimizing inference delay is crucial. High throughput ensures that the system can handle large volumes of inference requests without bottlenecks, improving user experience and system reliability. 

Dynamic Batching 

Inferentia supports dynamic batching, a technique that groups multiple inference requests together before processing them. This maximizes hardware utilization, reduces idle time, and improves overall inference efficiency. Dynamic batching is particularly useful for applications where multiple user queries arrive simultaneously, such as customer support chatbots, recommendation systems, and video analytics. By efficiently managing computational resources, Inferentia ensures that each batch of requests is processed quickly, reducing overall response times and costs. 

Leveraging AWS NeurON SDK for Model Compilation 

The AWS NeurON SDK plays a crucial role in optimizing models for Inferentia. With NeurON Compiler, deep learning models are converted into an optimized format that takes full advantage of Inferentia’s architecture. This process enhances inference speed, reduces memory overhead, and ensures that models run as efficiently as possible.

Developers can use NeurON to fine-tune models, leverage quantization techniques (such as BF16 and INT8), and deploy AI workloads at scale with minimal performance loss. The NeurON SDK helps developers seamlessly integrate Inferentia into their workflows, enabling cost-efficient, high-performance AI deployments. 

Benchmarking AWS Inferentia for Multimodal Models 

Performance Comparisons 

AWS has benchmarked Inferentia against GPUs for several multimodal models. Here are some results: 

• BERT-Large on Inferentia vs. NVIDIA T4 GPU: Inferentia delivered 30% lower cost per inference. 

• ResNet-50 on Inferentia vs. NVIDIA V100: Inferentia reduced inference costs by 45%. 

• GPT-based multimodal models: Inferentia handled NLP and vision workloads with higher energy efficiency than traditional GPUs. 

Best Practices for Using AWS Inferentia for Multimodal Inference 

• Optimize Model Precision: Use BF16 or INT8 quantization to improve inference speed while reducing memory usage. 

• Use Model Parallelism: Inferentia allows large models to be split across multiple chips for efficient parallel execution. 

• Leverage Dynamic Batching: Batch multiple inference requests together to maximize resource utilization. 

• Optimize with AWS Neuron SDK: Use Neuron Compiler and Neuron Runtime to fine-tune models for Inferentia, improving performance and reducing costs. 

• Deploy with AWS Inferentia-powered EC2 Inf1 Instances: Deploy inference workloads on Inf1 instances instead of GPU-based instances to benefit from lower costs and higher efficiency.