Case Studies

Optimising AI inference for performance and efficiency

Written by Damanpreet Kaur Vohra | May 28, 2024 9:00:00 AM

AI Inference is critical in various applications, such as computer vision, natural language processing, and predictive analytics. Slow inference can lead to unacceptable latencies, while inefficient hardware utilisation can result in high computational costs and energy consumption. Many organisations' AI models are deployed on hardware unoptimised for inference, leading to significant performance gaps. This blog explores techniques for AI inference acceleration and leveraging GPUs for AI hardware optimisation.

Understanding AI Inference

AI Inference takes a pre-trained model and applies it to new input data to generate predictions or decisions. This is the fundamental step that allows AI models to be used in real-world applications after the initial training phase. The types of AI models used for inference can be broadly categorised into deep learning and traditional machine learning models. 

Deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are particularly effective for tasks like computer vision, natural language processing, and speech recognition. These models have multiple layers of artificial neurons and can learn complex patterns and representations from large datasets.

On the other hand, traditional machine learning models, like decision trees, random forests, and support vector machines, are often used for structured data tasks such as predictive analytics, recommendation systems, and fraud detection. These models rely on carefully engineered features and statistical techniques to make predictions. However, the inference process requires specialised hardware and software for optimal performance and efficiency. From a hardware perspective, AI inference can be computationally intensive, especially for deep learning models, and may require high-performance processors like GPUs. These hardware components are designed to perform the parallel matrix and tensor operations common in AI workloads with high throughput and low latency.

If we talk about AI Inference software, it typically relies on frameworks and libraries like TensorFlow, PyTorch, ONNX Runtime, or vendor-specific solutions like NVIDIA TensorRT. These AI Inference software tools provide optimised implementations of AI models, support various hardware targets, and offer tools for model optimisation, quantisation and deployment.

Challenges in AI Inference

For better AI Inference efficiency, we must identify the challenges associated with the process. 

  1. Computational Intensity: AI models are computationally intensive and require significant processing power and memory resources, with complex models containing millions or billions of parametres that can strain traditional hardware, leading to slow inference times.
  2. Model Size and Memory: State-of-the-art AI models can be extremely large, exceeding billions of parametres, making it challenging to store and load these massive models into memory, especially on resource-constrained devices. Even memory bandwidth and efficient data transfer can become bottlenecks during inference.
  3. Real-Time Inference Demands: Many applications, such as autonomous vehicles, video analytics, and real-time translation, require low-latency inference with minimal delays to ensure smooth and responsive user experiences. Achieving high throughput and low latency simultaneously can be challenging.
  4. Power Consumption and Energy Efficiency: AI inference can be energy-intensive, leading to high power consumption and heat dissipation, making power and energy efficiency critical considerations for deploying AI inference on battery-powered devices or in data centres with high operational costs. At Hyperstack, we run at peak efficiency and our equipment is over 20x more energy-efficient than traditional computing.

Similar Read: How Energy-Efficient Computing for AI Is Empowering Industries

Optimising AI Inference: Hardware Acceleration with GPUs

GPU acceleration is a promising solution to mitigate the above-mentioned challenges in AI inference. GPUs are ideal for matrix multiplication and other compute-intensive tasks fundamental to AI inference. By offloading inference workloads to GPUs, developers can achieve significant speedups, often ranging from 5-20x compared to CPU-based inference. This acceleration enables real-time inference, making it suitable for applications like:

  • Computer Vision
  • Natural Language Processing
  • Autonomous Vehicles
  • Healthcare
  • Finance

Benefits of GPU-Accelerated AI Inference

The benefits of utilising GPUs for high-performance AI Inference include: 

  • Reduced Latency: GPUs can process large batches of inputs in parallel, drastically reducing the time required for inference compared to CPUs, making them ideal for low-latency applications.
  • Increased Throughput: The high compute density and parallelism of GPUs enable the processing of multiple inputs simultaneously, increasing the overall throughput of the inference pipeline.
  • Energy Efficiency: While GPUs consume more power than CPUs, their specialised architecture and efficient parallelisation can lead to better performance-per-watt for AI workloads.
  • Scalability: GPU-accelerated inference can be scaled across multiple GPUs or distributed across multiple servers, enabling high-performance and cost-effective solutions for large-scale deployments.

GPU Solutions and Frameworks

NVIDIA offers numerous solutions for high performance AI Inference through various libraries and GPUs:

  • CUDA: A parallel computing platform and programming model for NVIDIA GPUs, providing optimised libraries and tools for AI workloads.
  • TensorRT: A high-performance deep learning inference optimiser and runtime for deploying models on NVIDIA GPUs, offering techniques like kernel auto-tuning, layer fusion, and precision calibration.
  • NVIDIA GPUs: At Hyperstack, we offer access to a wide range of NVIDIA GPUs optimised for AI Inference including high-end data centre GPUs like the NVIDIA A100 and NVIDIA H100. We are also one of the first global providers to offer reservation access to NVIDIA Blackwell GPUs like the NVIDIA DGX B200 equipped with 144 petaFLOPS inference performance for maximum speed and efficiency, ideal for AI Inference. 

Optimisation Techniques for GPU-Accelerated Inference

We have covered some optimisation techniques used by developers for GPU-Accelerated Inference: 

Model Optimisation

  • Model Quantisation: Reducing the precision of model weights and activations from 32-bit floating-point to lower precisions (e.g., 16-bit, 8-bit, or even lower) can significantly improve performance and memory efficiency.
  • Model Pruning: Removing redundant or insignificant weights from the model can reduce its size and computational complexity without significantly impacting accuracy.
  • Knowledge Distillation: Training a smaller "student" model to mimic the behaviour of a larger "teacher" model, enabling the efficient deployment of the smaller model.

Similar Read: How to Use Batching for Efficient GPU Utilisation

Data Optimisation

  • Data Formatting: Optimising the format and layout of input data to align with GPU memory access patterns can improve data transfer efficiency and reduce bottlenecks.
  • Data Batching: Processing multiple inputs as a batch can amortise the overhead of launching inference and improve GPU utilisation.

Software Optimisation

  • GPU Kernel Optimisation: Fine-tuning GPU kernels for specific models and hardware can improve performance and efficiency.
  • Kernel Fusion: Fusing multiple operations into a single kernel can reduce memory access and improve overall throughput.
  • Asynchronous Execution: Overlapping data transfers with computation can hide latencies and improve overall pipeline efficiency.

Hardware Optimisation

Choosing the right GPU hardware based on performance, memory, and power requirements can optimise cost and efficiency. We offer a range of NVIDIA GPUs specifically designed for accelerating AI inference workloads. These include the powerful NVIDIA A100 GPU and NVIDIA H100, which deliver exceptional performance for deep learning tasks. We also offer access to the latest generation of NVIDIA Blackwell GPUs optimised for AI and high-performance computing.

Real-World Examples

Here are some real-world examples of successful implementations of optimised AI inference using GPU acceleration:

  • Autonomous Vehicles: Waymo leverages GPU-accelerated inference for their self-driving car systems. With NVIDIA GPUs, Waymo's vehicles can process sensor data and make real-time decisions with low latency, ensuring safe navigation. Performance gains include reduced inference times from seconds to milliseconds, enabling real-time decision-making.
  • Natural Language Processing: OpenAI uses GPU-accelerated inference for their Chat GPT language models. With NVIDIA's Tensor Cores and optimised libraries, OpenAI efficiently deploys large language models. Significant reductions in inference times enable faster response times. 
  • Medical Imaging: NVIDIA Clara accelerates AI inference for medical imaging tasks like CT and MRI scans using GPUs and TensorRT. Healthcare providers can achieve real-time inference for image analysis and diagnosis, reducing inference times from minutes to seconds. Read more to understand the Role of GPU in Healthcare. 
  • Video Analytics: NVIDIA DeepStream leverages GPU acceleration to process multiple video streams in real-time for applications like surveillance and traffic monitoring. It delivers reduced latency and increased throughput for processing multiple streams simultaneously. 

Developments in AI Inference

The demand for efficient and high-performance AI inference is driving the development of specialised hardware solutions. Leading companies like NVIDIA introduced dedicated AI accelerators, such as the NVIDIA Blackwell GPUs, designed for AI inference workloads. The NVIDIA DGX B200 GPU is well-suited for AI inference. Some of the key features that make it ideal for AI inference include:

  • High Compute Performance: The NVIDIA Blackwell DGX B200 GPU offers up to 20 petaflops of FP4 compute performance, making it suitable for demanding AI workloads.
  • Improved Transformer Engine: The second-generation transformer engine in Blackwell doubles compute, bandwidth, and model size, enabling faster AI training and inference.
  • Efficient Memory: The NVIDIA Blackwell GPU features 192GB of HBM3e memory, offering 8 TB/s of bandwidth, essential for AI workloads requiring large amounts of data.
  • Scalability: The Blackwell architecture supports multi-node configurations, enabling seamless scaling for large AI models.
  • Low Power Consumption: NVIDIA Blackwell reduces cost and energy consumption by up to 25x compared to previous generations, making it a power-efficient solution for AI inference.
  • Support for Various Precision Formats: Blackwell supports multiple precision formats, including FP4, FP8, and TF32, allowing users to choose the optimal format for their AI workloads.
  • Seamless Integration: Blackwell is designed to work with Nvidia's Grace CPU and other components, enabling easy integration into existing AI infrastructure.

The advancements in optimising AI inference have far-reaching implications across various industries and applications. In the automotive and robotics sectors, real-time, low-latency inference is crucial for safer and more responsive autonomous systems like self-driving cars and robots. Similarly, efficient inference in healthcare and medical imaging can accelerate medical image analysis, diagnosis, and treatment planning, leading to improved patient outcomes and faster decision-making. Retail and customer analytics can also benefit from optimised inference, enabling real-time video analytics, customer behaviour analysis, and personalised recommendations, driving better customer experiences and operational efficiencies. 

Conclusion

As these developments keep growing, we can expect AI inference to become more efficient, faster and more accessible across a wide range of industries and applications. The combinations of hardware innovations, model optimisation techniques and software advancements will drive the next generation of AI-powered systems and services. This will lead to more intelligent and responsive solutions that can transform businesses. 

At Hyperstack we offer industry-leading solutions for building, deploying and scaling AI applications for your business. Get started to access the powerful NVIDIA GPUs that deliver maximum performance and efficiency with our cost-effective prices. 

FAQs

What are the main benefits of using GPUs for AI inference?

GPUs offer significant performance gains for AI inference tasks due to their highly parallel architecture and specialised hardware for matrix operations. Key benefits include reduced latency, increased throughput, energy efficiency, and scalability. 

How does GPU acceleration benefit real-time inference applications?

Real-time inference applications, such as autonomous vehicles, video analytics, and natural language processing, require low-latency and high-throughput performance. GPU acceleration is crucial in these scenarios as it enables real-time decision-making and responsive user experiences. 

What is the best GPU for AI inference?

We recommend using the NVIDIA A100, H100 and the upcoming Blackwell NVIDIA DGX B200. It offers up to 20 petaflops of FP4 compute performance, a second-generation transformer engine for faster inference, 192GB of HBM3e memory, and support for various precision formats like FP4, FP8, and TF32. 

 
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