Introduction

Generative AI has revolutionized numerous industries, from content creation to scientific research. However, the true potential of these powerful models has been limited by the time and resources required for inference - the process of generating outputs from trained models.

Enter fast inference: a technological advancement that's set to redefine the landscape of AI applications.

What is Inference in Generative AI?

Inference in generative AI refers to the process of using a pretrained models, such as chatGPT, Claude, to create new outputs based on input data. This could involve generating text, images, audio, or other forms of content. Traditionally, this process has been computationally intensive, often requiring significant time and resources.

The Importance of Speed in AI Applications

In today's fast-paced digital world, speed is crucial. Users expect instant responses, whether they're interacting with a chatbot, generating images, or using AI-powered tools in their workflow. Slow inference times can lead to poor user experiences, reduced productivity, and limited adoption of AI technologies.

Why Inference Feels Slow?

When we are using chatGPT, Claude or other services which are relying traditional technologies, such as GPUs, it feels slow. Because Inference is a sequential process where each word must be generated before the next one can begin.

Inference requires large amount of memory bandwidth between memory where model is hosted and the compute core where the actual mathematics happens for AI.

In above example, we are using one of the LLaMA 70B model. Typically, the memory requirement for model is 2x the size of the number of parameters. So 70B model requires approx. 140GB of memory. Typical GPUs have memory of 80GB so they are short of at least 60 GB of memory. Additionally, to generate 1,000 tokens/second, the memory bandwidth required is 140 terabytes/second. The most advanced GPU, such as NVIDIA's H100 has 3.3 terabytes/second memory bandwidth - thus serving the tokens slowing than 1,000 tokens/second.

How Fast Inference is Changing the Game

Fast inference is transforming the AI landscape in several key ways:

1. Real-time interactions: With faster inference, AI models can respond in real-time, enabling more natural and fluid interactions between humans and AI systems.

2. Scalability: Quicker processing times mean that AI services can handle more requests, making it feasible to deploy AI at scale.

3. Cost-efficiency: Faster inference often translates to lower computational costs, making AI more accessible to a broader range of organizations and applications.

4. Improved user experience: Near-instantaneous responses from AI systems lead to better user experiences and increased user engagement.

5. Enabling new applications: Some AI applications are only feasible with very fast inference times, opening up new possibilities for AI integration in various fields.

Real-World Applications and Examples

Those who remembers the dial-up internet (e.g. AOL) in comparison to Fiber Optic Internet we have today is considered superfast. As we evolved from dial up internet to fast internet, suddenly we started seeing hundreds of applications on Internet which can leverage this fast internet - such as Netflix streaming, Zoom video calls, the list goes on and on.

Similarly, our current inference speed which is 100 to 200 tokens per second at best, what if we are able to 10x that speed or 20x? This will open up lot many newer possibilities:

Real-time language translation: Simultaneous interpretation for live speeches or events. Instant translation of live video content or streaming
Interactive AI assistants: More responsive chatbots for customer service. Voice-activated assistants with near-instantaneous responses
Content generation and analysis: Real-time content moderation for social media platforms. Instant generation of news summaries or reports
Augmented reality applications: Real-time text translation overlays in AR glasses. Context-aware information provision in AR environments
Financial trading: Real-time analysis of market news and trends. Automated trading systems with natural language understanding
Healthcare: Real-time analysis of medical records during patient consultations. Instant generation of medical reports or summaries
Gaming: Dynamic, AI-driven storylines and character interactions. Real-time generation of game content and dialogues
Education: Personalized, adaptive tutoring systems. Real-time feedback on student writing or problem-solving
Scientific research: Rapid analysis of research papers and data. Real-time hypothesis generation and testing
Legal and compliance: Real-time contract analysis and risk assessment. Instant legal research and case law analysis
Creative industries: Real-time collaborative writing assistance. Instant generation of script variations or story ideas
Autonomous vehicles: Real-time natural language processing for voice commands. Rapid decision-making based on complex environmental data
Smart cities: Real-time analysis of city-wide data for resource management. Instant response generation for citizen inquiries
Cybersecurity: Real-time threat detection and response based on natural language analysis. Instant generation of security reports and alerts

Take a look at this video someone created where two AI chatbots are interacting in voice. While this is a bit funny of how they take easily more than 2 minutes to say "goodbye" to each other, possibilities are enormous.

Where Is The Fast Inference?

You may be wondering, but Ritesh, where is such fast inference? All we see is 100 tokens per second.

Glad you asked. At Cerebras Systems (yes, I am bit biased), we have recently launched our Inference services where for some of the popular LLaMA models we are able to produce over 1,900 tokens per second (for LLaMA 8B model) and 481 tokens per second (for LLaMA 70B model) - which is the FASTEST and the most accurate on the Internet (as of this writing) - as validated by Artificial Analysis.

image credit: Cerebras Systems

Future Implications

The advent of fast inference is likely to accelerate the adoption and integration of AI across various sectors. We can expect to see more seamless AI-human interactions, more sophisticated real-time AI applications, and potentially new paradigms in computing that leverage the speed and power of AI.

Conclusion

Fast inference is indeed a game changer in the world of generative AI. By addressing one of the key limitations of AI deployment - speed - it's paving the way for more widespread, efficient, and innovative use of AI technologies. As we continue to push the boundaries of what's possible with AI, fast inference will undoubtedly play a crucial role in shaping the future of this transformative technology.

What problems do you think fast inference can solve?

Fast Inference in Generative AI: A Game Changer