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Reading list

What is Deep Learning? DL vs ML vs AI Why Deep Learning is So Popular?
Applications of Deep Learning Multi Layer Perceptron Visualizing the Neural Network Understanding Decision Boundary Forward and Backward Propagation Intuition
Applications of Deep Learning Multi Layer Perceptron Visualizing the Neural Network Understanding Decision Boundary Forward and Backward Propagation Intuition
Gradient Descent Algorithm Variants of Gradient Descent Algorithm
Introduction to Loss Function Binary and Categorical Cross Entropy
Why Do We Need Activation Functions? Linear Activation Function Sigmoid and Tanh Function Softmax How to Select Right Activation Function?
Perceptron Weights in Perceptron Introduction to Artificial Neural Network
Understanding Forward Propagation Mathematically Understand Backward Propagation Mathematically Implementing Neural Networks in Python
Problems with Gradient Descent Gradient Descent with Momentum Adagrad and Adadelta
Introduction to Learning Rate Schedulers
Overview of Deep Learning Frameworks Implementing Neural Networks using Keras Functional API in Keras Implementing Neural Networks using Keras Hyperparameter Tuning of MLP in Keras
Understanding Early stopping Understanding Dropout Vanishing and Exploding Gradients Weights Initialization Techniques Implementing Weight Initializing Techniques Batch Normalization Image Augmentation Techniques Image Generator and Fit Generator Model Checkpointing Implementing Model Checkpointing Dealing with Class Imbalance Ensemble Deep Learning
Introduction to GPU and TPU TensorRT
Introduction to Unsupervised Learning How to Solve Unsupervised Learning Problems? Introduction to Autoencoders Implementing Autoencoders
A Beginners Guide to Codeless Deep Learning
TensorFlow Serving Build Deep Learning Models for Android
Introduction to PyTorch and Tensors Mathematical and Matrix Operations in PyTorch Important PyTorch Modules Implement CNN in PyTorch Transfer Learning in PyTorch Working with Text Data in PyTorch Building a RNN model in PyTorch Autoencoders in PyTorch

Nvidia Unleashes Game-Changing AI Chip to Turbocharge Generative AI Applications

K.C. Sabreena Basheer
K.C. Sabreena Basheer Last Updated : 10 Aug, 2023
3 min read

In an era where technology continuously pushes boundaries, Nvidia has once again left its mark. The company has launched the GH200 Grace Hopper Superchip, an advanced AI chip tailored to amplify generative AI applications. This latest innovation promises to revolutionize AI, offering enhanced performance, memory, and capabilities to propel AI to heights.

Also Read: China’s Hidden Market for Powerful Nvidia AI Chips

NVIDIA's GH200 Grace Hopper Superchip can enhance generative AI's performance, memory, and capabilities.

Unveiling the GH200 Grace Hopper: A New Era of AI

Nvidia, a renowned chip manufacturer, has introduced the world to its cutting-edge GH200 Grace Hopper platform, a next-generation AI chip designed for accelerated computing. This platform will tackle the most intricate generative AI workloads, ranging from complex language models to recommender systems and intricate vector databases.

Also Read: NVIDIA Builds AI SuperComputer DGX GH200

A Glimpse into Grace Hopper Superchips

At the heart of the GH200 Grace Hopper platform lies the revolutionary Grace Hopper Superchip. This groundbreaking processor incorporates the world’s first HBM3e (High Bandwidth Memory 3e) technology, offering a multitude of configurations to cater to diverse needs. Nvidia’s founder and CEO, Jensen Huang, lauds the platform’s remarkable memory technology, seamless performance aggregation through GPU connections, and an adaptable server design that facilitates data center-wide deployment.

Also Read: SiMa.ai to Bring World’s Most Powerful AI Chip to India

NVIDIA GH200 Grace Hopper Superchip architecture | Generative AI enhancement chip

Nvidia has once again outdone itself by enabling the Grace Hopper Superchip to connect with additional Superchips using Nvidia NVLink technology. This breakthrough connectivity empowers multiple chips to join forces, seamlessly deploying colossal generative AI models that were previously unimaginable. The high-speed NVLink ensures that the GPU gains full access to CPU memory, culminating in an impressive 1.2TB of fast memory in dual-chip configurations.

NVIDIA NVLink High-Speed GPU Interconnect

Amplified Performance: A Leap in Memory Bandwidth

The introduction of HBM3e memory is a pivotal moment in AI advancement. With a staggering 50% increase in memory bandwidth compared to its predecessor, HBM3e delivers an astonishing 10TB/sec combined bandwidth. This boost in memory capacity empowers the GH200 Grace Hopper platform to handle models 3.5 times larger than its predecessors while ensuring an unparalleled performance boost.

Also Read: Samsung Embraces AI and Big Data, Revolutionizes Chipmaking Process

Empowering Businesses: The Road Ahead

Leading system manufacturers are gearing up to release platforms based on the GH200 Grace Hopper in the second quarter of 2024. Nvidia’s unwavering commitment to empowering data centers and enhancing AI development is evident through this revolutionary product. As the AI landscape evolves, Nvidia’s hardware innovations continue to reshape the boundaries of possibility.

Also Read: Government Intervention in Chip Design: A Boon or Bane for India’s Semiconductor Ambitions?

The Grace Hopper Superchip enhances generative AI for businesses.

Revolutionizing AI Training and Inference

The GH200 Grace Hopper platform’s potential extends to the training and fine-tuning large language models. With the ability to handle models with hundreds of billions of parameters, and the prospect of soon exceeding a trillion parameters, Nvidia’s hardware innovation simplifies AI development. This advancement means AI models can be trained and referenced using compact networks, eliminating the need for extensive supercomputing resources.

Also Read: New AI Model Outshine GPT-3 with Just 30B Parameters

A Visionary Future for AI

Nvidia’s vision for AI transcends just hardware. As AI becomes more integral to our lives, Nvidia introduces a range of AI architectures catering to diverse AI needs. Collaborations with tech giants such as Microsoft, AWS & Google Cloud showcase Nvidia’s role as an AI hardware provider of choice. Nvidia’s commitment to AI infrastructure, efficiency, and performance is evident in its Spectrum-X Ethernet platform, which propels data processing and AI workloads in the cloud.

Our Say

By launching the GH200 Grace Hopper platform, Nvidia has once again proven its prowess in driving the AI revolution. Nvidia empowers AI developers and businesses to push boundaries and explore new horizons by providing unparalleled memory, performance, and connectivity. As Nvidia’s innovations continue to shape the AI landscape, we can anticipate an era of unprecedented AI achievements and discoveries.

K.C. Sabreena Basheer
K.C. Sabreena Basheer

Sabreena Basheer is an architect-turned-writer who's passionate about documenting anything that interests her. She's currently exploring the world of AI and Data Science as a Content Manager at Analytics Vidhya.

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