Predibase, the developer platform for productionizing open source AI, is debuting the Predibase Inference Engine, a comprehensive solution for deploying fine-tuned small language models (SLMs) quickly and efficiently. Designed for rapid, streamlined deployment across both private serverless (SaaS) and virtual private cloud (VPC) environments, the Predibase Inference Engine offers the most resource-efficient serving stack that empowers enterprises to scale specialized models at a fraction of the cost, according to the company.
Being able to demonstrate ROI is a key differentiator among successful AI initiatives and those unable to reflect the value of its investment. In the realm of fine-tuned, specialized models, this is even more crucial, as these domain-specific models often rely on excessive GPU resources to achieve its promise of high accuracy and better performance.
The Predibase Inference Engine—powered by Turbo LoRA and LoRAX to dramatically enhance model serving speed and efficiency—offers seamless GPU autoscaling, serving fine-tuned SLMs 3-4x faster than traditional methods and handling enterprise workloads of hundreds of requests per second, according to the vendor. The unique technical combination of multi-LoRA serving (LoRAX) and high throughput (Turbo LoRA and FP8 quantization) enables the Predibase Inference Engine to not only be more cost-effective but also optimized for scale.
“Unlike competitors, which are less efficient for fine-tuned models and struggle to scale, Predibase enables enterprises to serve more models with fewer resources,” explained Dev Rishi, CEO and co-founder at Predibase. “On top of that, it’s available in both our cloud and customer VPCs, providing flexibility for enterprises to deploy where it makes the most sense for their infrastructure and compliance needs.”
The types of enterprises that would benefit most from the Predibase Inference Engine are those with use cases that rely on real-time decision making and highly specialized AI models, such as in customer service automation, background checks and fraud detection, financial services, and marketing tech verticals.
“Businesses that need to process complex, high-volume data streams to deliver personalized recommendations or insights in real-time will find the high throughput and efficiency of Predibase essential for scaling their AI models efficiently and affordably,” noted Rishi.
Centered around the ideals of scalability, reliability, and control, the Predibase Inference Engine offers the following advantages:
- Deployable in private cloud environments so enterprises can expand the value of existing cloud commitments while benefiting from Predibase’s power and performance
- Guaranteed GPU capacity by reserving GPU resources from Predibase’s fleet of A100 and H100 GPUs, ensuring that mission-critical apps maintain sufficient burst capacity to meet service-level agreements (SLAs)
- Cold start optimization by scaling additional GPUs to handle burst capacity, mitigating against cold start delays amid traffic spikes to ensure a seamless user experience
- Multi-region high availability that enables customers to deploy mission-critical workloads throughout multiple regions, protecting from outages by autoscaling GPU needs
- Intuitive UI that centralizes the management and monitoring of SLM fine-tuning and serving with robust performance dashboards
“What truly sets Predibase apart is how it frees up valuable team time. By leveraging Predibase’s infrastructure, companies no longer need to invest heavily in building and maintaining their own complex serving stack,” said Rishi. “Predibase’s solution allows teams to focus on innovation and applying AI to their core business needs rather than wrestling with infrastructure challenges. Whether deployed in our cloud or in a customer's VPC, Predibase simplifies AI deployment while offering enterprise-grade performance, scalability, and security.”
Readers can try out Predibase’s innovations for free in its trial environment.
To learn more about Predibase, please visit https://predibase.com/.