The importance of AI is rapidly growing in our world. It enables how we unlock our phones with face ID, how we organize our days with auto-schedulers, and what music we listen to through recommendation systems. Models make these decisions—whether or not to open your device, to designate focused work time from 1-3PM, to play you the latest from Charli XCX—not through directly observing your work or listening habits, but from evidence and reasoning based on its inputs.
The process through which models draw these conclusions is called AI Inference. At Hyperbolic, we are building an AI ecosystem of the people, by the people, for the people, where AI inference brings positive value to humanity.
Here we'll delve deeper into the lifecycle of AI inference and explore how Hyperbolic is revolutionizing the way these impacts are brought to life.
The AI Life Cycle: Training vs. Inference
To understand inference, it's helpful to contrast it with training. During the training phase, an AI model learns the patterns in vast amounts of data. This often involves specialized processing and adjusting its assumptions about the data based on how accurately it initially predicts these patterns.
Inference puts this training to work by taking in unseen data and using what it learned to generate outputs or make decisions based on what it learned. Typically, the flow looks like:
Input: Receiving new data (text, image, audio, etc.)
Processing: Running that data through the trained model
Output: Generating predictions or results
This is different from simply looking up an answer in a database or following strict rules. Instead, the model is making an informed conclusion based on its training, just as humans do when we apply our learned knowledge to new situations.
The Hyperbolic Difference in AI Inference
While training AI models often requires significant computational resources, efficient inference is crucial for real-world applications. Fast, accurate inference enables AI to be deployed in scenarios where quick responses are essential, such as autonomous vehicles or real-time language translation. Moreover, efficient inference reduces the computational cost and energy consumption of AI applications, making them more sustainable and accessible.
Decentralized solutions for running inference are powerful alternatives to traditional centralized models. Hyperbolic’s decentralized AI Inference Engine leverages our network of coordinated, distributed GPU resources. This allows us to run inference on models in such a way that is:
Scalable: Our decentralized network can easily scale to meet the demands of any sized model, from a classification model to a base model, ensuring consistent performance even at peak usage.
Cost-effective: By utilizing our distributed network of GPU resources, we significantly reduce costs compared to centralized inference solutions.
Low latency: With inference running closer to the end user, response times are dramatically improved.
Private: Our decentralized GPU network enhances data privacy by reducing the need to transmit sensitive information to centralized servers that can store data.
Democratized: By making AI inference open and affordable, Hyperbolic’s decentralized inference engine allows builders the freedom to access and deploy AI technologies faster and at larger scales.
AI inference is the crucial step that brings AI from theory into practice. As we continue to push the boundaries of what's possible in AI, efficient and accessible inference plays an increasingly important role. Hyperbolic is at the forefront of this revolution, making powerful AI inference capabilities available to our diverse community of builders, opening up the possibilities of how AI can benefit humanity.
Take your ideas Hyperbolic and run your inference with us by joining our community of builders shaping the future of AI innovation. Head to app.hyperbolic.xyz/models to get started.
