Call for Beta Testers: Building a Better Inference Engine

Call for Beta Testers: Building a Better Inference Engine

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GMI Cloud is announcing beta testing for GMI Cloud Inference Engine – our proprietary inference engine at the heart of a trailblazing LLM operating system offering clients unprecedented customization and functionality. Inference engines are a critical part of AI infrastructure as they enable the practical application of AI models and inference at scale. Moving forward, the best inference engines will allow companies to create personalized AI strategies that can grow beside them.

The Current State of Inference Engines

Inference costs represent a significant portion of the overall expense in AI operations, often surpassing the costs of model training due to the sheer scale at which inferences must be run in production environments. Each real-time prediction, classification, or decision made by an AI model incurs computational and resource costs, which can escalate quickly for businesses with high user traffic or data processing demands. Lowering inference costs has become a major focus for companies developing AI, as it directly impacts profitability and scalability. 

Inference engines are what make the inference process run, much like the engine of a racecar. Just as different racecars require finely tuned engines for specific conditions, businesses must choose the right inference engine to maximize performance and efficiency (read more about inference costs here).

By optimizing inference engines to reduce latency, improve hardware utilization, and minimize energy consumption, businesses can drastically cut operational expenses while delivering faster, more efficient AI services—giving them a critical edge in a competitive market.

Companies that understand their own needs and select the type of inference engine that best aligns with their requirements gain a strategic advantage, optimizing both cost efficiency and performance. By adopting innovations in inference engines and tailoring solutions to their unique use cases, businesses can outpace and outlast competitors while delivering faster, smarter, and more cost-effective AI services.

A recent article from the Financial Times highlights how Chinese companies are innovating in inference engine development by optimizing hardware, training models on smaller data sets, and leveraging cost-effective engineering talent. These strategies have reduced inference costs by up to 90% compared to U.S. counterparts.

The Evolving Landscape of Inference Engines

Until recently, inference engines were largely designed as one-size-fits-all solutions, forcing businesses to adapt their workloads to the limitations of these systems rather than the other way around. This approach has led to inefficiencies, as different industries and use cases demand tailored solutions to maximize performance and cost efficiency.

Here are the main types of inference engines:

• API-Based Deployment: Access AI models via hosted APIs, managed entirely by the provider. This is ideal for small businesses seeking quick integration with minimal setup for tasks like customer support or content generation.
• Private Deployment: Hosts the AI serving stack on-premises or in a private cloud, ensuring full control over security and customization. It is best for enterprises with sensitive data or stringent compliance needs.
• Hybrid Deployment: Combines fixed reserved infrastructure with elastic cloud resources for variable workloads. Perfect for businesses balancing steady performance with bursts in demand.

GMI Cloud is changing the game by making inference engines customizable, with a focus on hybrid deployment.

The GMI Cloud Inference Engine leverages hybrid deployment to strike the ideal balance between cost efficiency and performance, enabling businesses to handle dynamic workloads with precision. By combining fixed, reserved infrastructure for steady demands with elastic cloud resources for handling peaks, GMI's approach empowers companies to scale their AI operations effectively.

What Makes the GMICloud Inference Engine Different

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Organizations seeking inference engines prioritize several key factors to ensure their AI operations are both effective and sustainable.

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• Cost-Efficiency: Optimized resource utilization is a top priority. Tailored systems that align with specific use cases enable businesses to maximize GPU and compute efficiency, significantly reducing operational expenses.
• Performance: High throughput and low latency are essential, especially when running demanding AI models. Businesses need inference engines designed to handle complex workloads without compromising speed or accuracy.
• Security: For industries handling sensitive data, secure custom deployment options are non-negotiable. Organizations value inference engines that offer complete control over their data and infrastructure, whether on-premises or in private cloud environments.
• Scalability: As businesses grow and workloads fluctuate, the ability to scale seamlessly is critical. Inference engines that adapt to increased demand without excessive costs or performance degradation provide a clear competitive advantage.

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Our expert engineering team designed GMI Cloud’s Inference Engine with customization at the core of the offering. We examined the landscape of inference engine providers and saw that large players (e.g., Fireworks, Together AI) may offer valuable features such as serverless, on-demand APIs but are limited in their ability to customize to client needs. 

With customization at the forefront of our offering, GMI Cloud’s edge is in its ability to fine-tune models to suit proprietary enterprise needs for a wide host of bespoke applications—from voice agents to image and video generation to more niche use cases like medical imaging or fraud detection for financial services. 

Beta Testers Wanted

This soft launch of GMI Cloud’s Inference Engine is just the beginning and we are dedicated to making it the best product possible. To do so, we need YOUR help and participation in the beta testing for this platform.

1. What we’re looking for:
   • Feature feedback: Customization pain points and development flow challenges.
   • Requests that shape the roadmap for future iterations. This could be new features, UI/UX, anything that you think might make the inference engine better serve users like you.
2. Why join:
   • Influence the development of a product tailored to your needs.
   • Be part of a movement to reshape the AI infrastructure landscape.

Expert Insights from Yujing Qian, VP of Engineering at GMI Cloud

GMI Cloud is also proud to announce Yujing Qian as our new VP of Engineering. Yujing has been an integral part of GMI Cloud’s success up to this point and is the leader and visionary behind GMI Cloud's Inference Engine.

Throughout his career (which includes tenure at Google and mineral.ai), Yujing has displayed a real commitment to building powerful user-centric products and a passion for shaping the future of AI infrastructure. One of his mantras and common advice to younger engineers is to “focus on why you’re building a feature, not just the feature itself.” Yujing also draws inspiration from other engineers such as Jeff Dean, a pioneer behind innovations like TensorFlow and Google Brain, whose vision and engineering brilliance have shaped modern technology.

At the core of his engineering philosophy for GMI Cloud Inference Engine is a goal to help customers achieve faster time to market with tailored solutions. This means prioritizing customer feedback and concrete goals over vague feature ideas and emphasizing building core features first, with room to expand later. 

Closing Thoughts

We encourage companies of all types to join our beta testing phase and be part of shaping the future of inference engines.

With GMI Cloud, you’re not just adopting cutting-edge AI solutions—you’re partnering with a team dedicated to delivering full customization, unmatched flexibility, and hybrid deployment expertise tailored to your business needs. Let’s build the next generation of AI together.

Sign up to be part of the beta testing in the form below!