How GPUs enable machines to create novel and exciting content from data
Generative AI systems can use natural language processing and computer vision to create new text, images, video, audio, code, or synthetic data in response to prompts. For example, you can use ChatGPT, a free chatbot that can generate an answer to almost any question it’s asked, or Dall-E, a tool that can create images from a text description or generate text captions from images. Generative AI has many applications and benefits for different industries and domains, such as entertainment, education, healthcare, e-commerce, etc.
But how does generative AI work? And what makes it possible? The answer is GPUs, or graphics processing units.
GPUs are purpose-built chips originally designed for computer graphics, but their massively parallel designs make them ideal for doing generative AI processing as well. GPUs are essential for powering generative AI models and enabling them to learn from large amounts of data and generate high-quality content.
In this article, we will explore the latest developments and innovations in GPUs for generative AI, such as the H100 NVL, a new PCIe accelerator that can increase the performance of GPT-3 by twelve-fold over an A100. We will also discuss the current and potential use cases and benefits of generative AI for various industries and domains, as well as the ethical and social implications and risks of generative AI. By the end of this article, you will have a better understanding of how generative AI and GPUs are transforming the world of content creation and consumption.
The latest developments and innovations in GPUs for generative AI
GPUs are specialized hardware devices that can perform parallel computations much faster than CPUs. GPUs were originally designed for rendering graphics, but they have also proven to be very effective for deep learning, a branch of machine learning that uses neural networks to learn from data and generate outputs. Deep learning is the core technology behind generative AI, which can create new text, images, video, audio, code, or synthetic data from data.
To train and run generative AI models, GPUs are essential. GPUs have thousands of cores that can perform matrix operations in parallel, which are the basic operations of neural networks. GPUs also have high memory bandwidth and large memory capacity, which allow them to store and process large amounts of data efficiently. GPUs can also support various types of precision, such as 32-bit float, 16-bit float, or 8-bit integer, which can affect the speed and accuracy of generative AI models.
However, not all GPUs are equal when it comes to generative AI. Different GPUs have different features and specifications that can affect their performance and suitability for generative AI tasks. For example, some GPUs have Tensor Cores, which are specialized units that can perform mixed-precision matrix operations faster than regular cores. Tensor Cores can accelerate the performance of generative AI models that use large language models (LLMs), such as ChatGPT, which can generate natural language text from prompts. Other GPUs have RT Cores, which are specialized units that can perform ray-tracing calculations faster than regular cores. RT Cores can accelerate the performance of generative AI models that use graphics rendering techniques, such as Omniverse, which can create photorealistic virtual worlds from text inputs.
In this section, we will compare the features and specifications of different GPUs for generative AI, such as the L4, L40, A100, and H100. We will also highlight the advantages and challenges of using GPUs for generative AI, such as speed, scalability, cost, availability, etc.
L4
The L4 is a low-end GPU that is suitable for entry-level generative AI tasks. It has 4 GB of GDDR6 memory and 1280 CUDA cores. It does not have Tensor Cores or RT Cores. It has a memory bandwidth of 192 GB/s and a peak performance of 5.3 TFLOPS (FP32) or 10.6 TFLOPS (FP16). It consumes up to 75 W of power and does not require external power connectors.
The L4 is a good choice for beginners who want to experiment with generative AI without spending too much money. It can run some basic generative AI models such as StyleGAN2, which can generate realistic images of faces or objects. However, it may struggle with more complex or larger models that require more memory or compute power.
L40
The L40 is a mid-range GPU that is suitable for mainstream generative AI tasks. It has 8 GB of GDDR6 memory and 2560 CUDA cores. It has 20 Tensor Cores and 20 RT Cores. It has a memory bandwidth of 320 GB/s and a peak performance of 10 TFLOPS (FP32) or 20 TFLOPS (FP16). It consumes up to 160 W of power and requires one 8-pin power connector.
The L40 is a good choice for enthusiasts who want to run more advanced generative AI models such as ChatGPT, which can generate natural language text from prompts. It can also run some graphics rendering models such as Omniverse, which can create photorealistic virtual worlds from text inputs. However, it may not be able to handle very large or complex models that require more memory or compute power.
A100
The A100 is a high-end GPU that is suitable for professional generative AI tasks. It has 40 GB of HBM2e memory and 6912 CUDA cores. It has 432 Tensor Cores and 72 RT Cores. It has a memory bandwidth of 1555 GB/s and a peak performance of 19.5 TFLOPS (FP32) or 39 TFLOPS (FP16). It consumes up to 250W of power and requires two 8-pin power connectors.
The A100 is a good choice for professionals who want to run state-of-the-art generative AI models such as StyleGAN3, which can generate high-resolution images of faces or objects with more realism and diversity. It can also run some large language models such as GPT-3, which can generate natural language text from prompts. However, it may not be able to handle very large or complex models that require more memory or compute power.
H100 NVL
The H100 NVL is a new PCIe accelerator that is specially designed for large language model (LLM) inference. It consists of two H100 PCIe cards that are connected by an NVLink bridge. Each card has 94 GB of HBM3 memory and 16896 CUDA cores. Each card has 528 Tensor Cores and 72 RT Cores. Each card has a memory bandwidth of 3.9 TB/s and a peak performance of 67 TFLOPS (FP32) or 134 TFLOPS (FP16). The combined dual-GPU card has a memory bandwidth of 7.8 TB/s and a peak performance of 134 TFLOPS (FP32) or 268 TFLOPS (FP16). It consumes up to 800 W of power and requires four 8-pin power connectors.
The H100 NVL is a good choice for LLM users who want to deploy very large or complex models such as GPT-175B, which has 175 billion parameters and can generate natural language text from prompts. The H100 NVL can increase the performance of GPT-175B by twelve-fold over an A100 while maintaining low latency in power-constrained data center environments. The H100 NVL also supports FP8 precision, which can further boost the performance and efficiency of LLM inference.
Advantages and challenges of using GPUs for generative AI
Using GPUs for generative AI has many advantages, such as:
- Speed: GPUs can perform parallel computations much faster than CPUs, which can reduce the training and inference time of generative AI models.
- Scalability: GPUs can be connected by high-speed interconnects such as NVLink, which can enable efficient scaling of generative AI models across multiple GPUs or nodes.
- Flexibility: GPUs can support various types of precision, such as FP32, FP16, TF32, FP8, etc., which can allow users to trade-off between the accuracy and performance of generative AI models.
- Innovation: GPUs are constantly evolving and improving, which can enable new possibilities and solutions for generative AI tasks.
However, using GPUs for generative AI also has some challenges, such as:
- Cost: GPUs are expensive devices that require high upfront investment and maintenance costs. They also consume a lot of power and generate a lot of heat, which can increase the operational costs and environmental impact of using GPUs for generative AI.
- Availability: GPUs are in high demand but low supply, which can make it difficult to obtain or upgrade GPUs for generative AI tasks. They also face competition from other types of hardware devices such as TPUs, FPGAs, ASICs, etc., which may offer better performance or efficiency for certain generative AI tasks.
- Complexity: GPUs are complex devices that require specialized software and hardware tools to optimize and manage them. They also require skilled programmers and engineers to develop and deploy generative AI models on them.
SEE: Generative AI for Semiconductor Manufacturing: Challenges and Opportunities
How generative AI can create value and impact for various industries and domains
Generative AI is not only a fascinating technology, but also a powerful one. Generative AI can create value and impact for various industries and domains by enabling new possibilities and solutions that were previously unimaginable or impractical. Generative AI can offer additional advantages to businesses and entrepreneurs, including:
- Easily customizing or personalizing marketing content
- Generating new ideas, designs or content
- Writing, checking and optimizing computer code
- Drafting templates for essays or articles
- Enhancing customer support with chatbots and virtual assistants
- Facilitating data augmentation for machine learning models
- Analyzing data to improve decision-making
- Streamlining research and development processes
In this section, we will explore some of the current and potential use cases and benefits of generative AI for various industries and domains, such as entertainment, education, healthcare, e-commerce, etc.
Entertainment
Generative AI can create novel and engaging content for entertainment purposes, such as music, art, games, movies, etc. For example, generative AI can create concept art, storyboards, virtual worlds, personalized content, etc. from text inputs. Generative AI can also enhance existing content by adding effects, animations, sounds, etc. Generative AI can also help artists and creators to express their creativity and collaborate with other artists and creators.
Some examples of generative AI applications in entertainment are:
- Jukebox: A neural network that can generate music in different genres and styles from lyrics or audio samples.
- Artbreeder: A web-based tool that can generate photorealistic images of faces, landscapes, animals, etc. from user inputs.
- Omniverse: A platform that can create photorealistic virtual worlds from text inputs using graphics rendering techniques.
- Replika: A chatbot that can create personalized conversations with users based on their preferences and emotions.
Education
Generative AI can create educational content and tools that can enhance learning outcomes and experiences for students and teachers. For example, generative AI can create quizzes, summaries, explanations, feedback, etc. from text or audio inputs. Generative AI can also create interactive simulations, scenarios, games, etc. that can facilitate experiential learning. Generative AI can also help teachers to design curricula, assess students’ progress, provide personalized guidance, etc.
Some examples of generative AI applications in education are:
- Quizlet: A web-based tool that can generate flashcards, quizzes, games, etc. from text inputs.
- SummarizeBot: A chatbot that can generate summaries of articles, books, podcasts, videos, etc. from text or audio inputs.
- Socratic: A web-based tool that can generate explanations and solutions for math problems from text or image inputs.
- Minecraft Education Edition: A game-based platform that can create immersive learning environments for students to explore various subjects.
Healthcare
Generative AI can create medical content and tools that can improve healthcare outcomes and experiences for patients and providers. For example, generative AI can create synthetic data that can augment existing data sets for machine learning models. Generative AI can also create new drugs and materials that can target specific properties or diseases. Generative AI can also create diagnostic tools that can analyze medical images or signals. Generative AI can also create therapeutic tools that can provide emotional support or behavioral interventions.
Some examples of generative AI applications in healthcare are:
- Insilico Medicine: A company that uses generative AI to discover novel drugs for various diseases.
- DeepMind AlphaFold: A neural network that can predict the 3D structure of proteins from amino acid sequences.
- Caption Health: A company that uses generative AI to guide users to perform ultrasound scans.
- Woebot: A chatbot that uses generative AI to provide cognitive behavioral therapy for mental health issues.
E-commerce
Generative AI can create e-commerce content and tools that can enhance customer satisfaction and loyalty. For example, generative AI can create product descriptions, reviews, recommendations, etc. from text or image inputs. Generative AI can also create product images or videos that can showcase different features or variations of the products. Generative AI can also create personalized offers or discounts that can match customers’ preferences or needs.
Some examples of generative AI applications in e-commerce are:
- Shopify Magic: A web-based tool that can generate product descriptions, reviews, recommendations, etc. from text or image inputs.
- Artbreeder: A web-based tool that can generate product images or videos that can showcase different features or variations of the products.
- ChatGPT: A free chatbot that can generate personalized offers or discounts that can match customers’ preferences or needs.
- UltimateGPT: A chatbot that can have more natural-sounding conversations with customers and provide them with support, guidance, and feedback.
The Ethical and social implications and Risks of generative AI
Generative AI is not only a powerful technology, but also a risky one. Generative AI can pose ethical and social implications and risks for individuals, organizations, and society at large. Some of these implications and risks include:
- Privacy and surveillance: Generative AI can be used to collect, analyze, and exploit personal data without consent or transparency. Generative AI can also be used to create synthetic media, such as images, videos, and audio, that can impersonate or manipulate people’s identities, behaviors, or opinions. Such AI-generated content can be difficult or impossible to distinguish from real media, posing serious threats to trust, security, and democracy.
- Bias and discrimination: Generative AI can be influenced by human biases that are embedded in the data, algorithms, or systems that are used to train or run them. Generative AI can also amplify or create new biases that are not present in the original data or algorithms. Such biases can lead to unfair or harmful outcomes for certain groups or individuals based on their characteristics, such as race, gender, age, etc.
- Accountability and responsibility: Generative AI can be complex and opaque, making it difficult to understand how they work or why they produce certain outputs. Generative AI can also be autonomous and adaptive, making it difficult to control or predict behaviors or consequences. Such complexity and opacity can raise challenges for assigning accountability and responsibility for the actions or outcomes of generative AI systems.
- Creativity and originality: Generative AI can challenge the notions of creativity and originality by producing content that mimics or surpasses human-generated content. Generative AI can also raise questions about the ownership and authorship of the content they generate, as well as the intellectual property rights and moral rights of the creators and users of such content.
- Human dignity and agency: Generative AI can affect human dignity and agency by replacing or augmenting human roles and functions in various domains. Generative AI can also influence or manipulate human emotions, preferences, or decisions by generating persuasive or deceptive content. Such effects can impact the autonomy, self-worth, and well-being of humans.
- Social norms and values: Generative AI can shape social norms and values by creating content that reflects or challenges the existing cultural, moral, or ethical standards. Generative AI can also create content that promotes or undermines certain social causes or movements. Such impacts can affect the social cohesion, diversity, and justice of society.
Conclusion
Generative AI is a type of artificial intelligence technology that can produce various types of content from data, such as text, images, video, audio, code or synthetic data. Generative AI has many applications and benefits for different industries and domains, such as entertainment, education, healthcare, e-commerce, etc. However, generative AI also poses ethical and social implications and risks for individuals, organizations, and society at large, such as privacy and surveillance, bias and discrimination, accountability and responsibility, creativity and originality, human dignity and agency, and social norms and values. Therefore, it is important to develop and deploy generative AI responsibly and safely, following the best practices and guidelines that are available or being developed by various initiatives and organizations. Generative AI is a powerful and promising technology that can transform the world of content creation and consumption, but it also requires careful and critical consideration of its impacts and implications.
