Cerebras is revving up its engines in the race against Nvidia and other AI chip startups, as it gears up for a potential IPO. With the announcement of its filing, Cerebras aims to supercharge its capabilities to compete in the high-stakes world of generative AI. This is not just about speed; it’s about setting the pace in an industry where every millisecond counts.
The Speed Battle in Generative AI
Cerebras, alongside competitors Groq and SambaNova, is in a fierce competition to claim the title of the “fastest generative AI.” All three companies are innovating rapidly, pushing the limits of their hardware and software. Their goal? To enable AI models to produce responses at lightning speed—outpacing even Nvidia’s GPUs.
What Is Inference?
When we interact with AI—like asking a question to a chatbot—this process is known as inference. During inference, AI models don’t simply shuffle through data; they break down questions into manageable pieces called tokens. Each token can represent a word or a fragment of a word, allowing the model to generate an answer efficiently.
The Quest for Ultra-Fast Inference
What does “ultra-fast” inference actually look like? If you’ve used AI chatbots like OpenAI’s ChatGPT or Google’s Gemini, you may find their response times impressive. However, in February 2024, Groq demonstrated a chatbot that produced responses at an astonishing rate of 500 tokens per second, and by May, SambaNova announced it had surpassed 1,000 tokens per second.
Cerebras recently claimed the title of “world’s fastest AI inference” by achieving a remarkable 2,000 tokens per second on one of Meta’s Llama models. This surge in speed is more than just a bragging right; it has practical implications for various applications.
Why Speed Matters in Generative AI
You might wonder, why is faster output crucial? When will we have enough speed? According to Cerebras CEO Andrew Feldman, the need for speed is growing as more applications leverage generative AI for tasks like search and streaming video. Latency can be a dealbreaker for businesses.
“Nobody’s going to build a business on an application that makes you sit around and wait,” Feldman told Fortune.
Complex Applications Drive Speed Requirements
Today’s AI models are being used in increasingly complex applications. For example, workflows that involve multiple AI agents can quickly escalate from a single query to numerous simultaneous requests. In such cases, performance becomes critical.
If a system is even slightly slow, it can lead to significant delays in output, hampering productivity. Fast inference unlocks new potentials, enabling applications to function seamlessly and deliver real-time results.
Unlocking AI Potential Through Speed
According to Mark Heaps, Chief Technology Evangelist at Groq, faster inference allows for greater application potential, particularly in data-heavy sectors like:
- Financial Trading: Instant insights can lead to timely decisions that impact revenue.
- Traffic Monitoring: Real-time data can improve city infrastructure and safety.
- Cybersecurity: Speed is crucial for identifying threats before they escalate.
Heaps argues that the race to boost speed will not only enhance quality and accuracy but will also provide a higher return on investment (ROI).
The Human Brain vs. AI Models
It’s important to note that AI models still trail the human brain in neural connections. As these models evolve, they’ll need to keep pace with their growing complexity. More advanced models require greater speed to remain functional and relevant.
Rodrigo Liang, CEO and co-founder of SambaNova, emphasises that inference speed is the turning point where all model training translates into business value. As the industry shifts focus from developing AI models to deploying them, the need for efficient token production becomes paramount.
Conclusion: The Fast Track to AI Innovation
Cerebras, Groq, and SambaNova are at the forefront of the generative AI race, demonstrating that speed is not just a feature; it’s a necessity. The demand for rapid inference will only increase as businesses integrate generative AI into their operations.
As these companies gear up for a new era of AI, their innovations could reshape the landscape, allowing for real-time insights and capabilities that were once unimaginable.
Cerebras’ impending IPO is not merely a financial move; it represents a strategic leap in the quest for generative AI supremacy. In this high-speed race, the question isn’t just about who is fastest—it’s about who can harness that speed to unlock the full potential of AI.
