Introduction
The growing demand for impactful, performant, and cost-effective AI solutions is non-negotiable for production AI success. In the burdgeoning GenAI space for example, enterprises deploying robust LLMs into production often underestimate the resources and scale needed. These challenges hinder success due to requirements for ensuring output accuracy, optimizing inference performance, and controlling infrastructure costs.
In this blog we will show how the capabilities of the Wallaroo Universal AI Inference Platform optimized for Arm®-based architectures on Ampere® directly addresses these production challenges by redefining how enterprises deploy, manage, and monitor AI workloads leading to efficient, cost effective applications that drive maximum business impact and ROI at factory scale.
Wallaroo’s Agentic AI Solution Powered by Ampere
The Wallaroo AI Inference Engine Optimized on Ampere
The Wallaroo AI Inference Engine delivers high-performance for both batch and real-time inference. The inference engine architecture features an optimized, multi-threaded network server and orchestration layer that manages incoming requests, prepares data, and executes models. Wallaroo offers flexible deployment, easy scaling and orchestration of Agentic AI inference microservices across GPU architectures and Arm-based architecture on Ampere, all through simple configurations.
Additionally, the Wallaroo AI inference engine, written in Rust, comes with built-in Autoscaling capabilities that adjust resource utilization using pre-configured triggers based on real-time demand, ensuring optimal inference latency and throughput, all while maximizing utilization of the underlying hardware (e.g. Ampere® Altra® or AmpereOne® processors) with built-in dynamic and concurrent batching capabilities
Wallaroo offers superior inference performance for critical Agentic AI use cases on Arm®-based hardware, both in the cloud and at the edge. Compared to bespoke or non-specialized AI inference solution implementations, Wallaroo significantly reduces unnecessary AI inference overhead using Ampere as seen in the benchmarks below.
“Amid today’s AI boom, customers are looking for more efficient and economical AI inferencing. With Wallaroo.AI’s migration to Ampere-based OCI A1 instances, we are providing them with a solution, bringing six times the AI inferencing performance while using significantly less money and power,” said Jeff Wittich, Chief Product Officer at Ampere. –
Learn More: Ampere Computing and Wallaroo.AI expand advanced AI options to OCI
Learn More: Deploy, Monitor & Manage AI models on Arm with Wallaroo
The Wallaroo AI Control Plane
The Wallaroo AI Control Plane is also designed to simplify and automate the entire AI production lifecycle with seamless integration into existing AI tools and ecosystems for launching AI models faster to production and maintenance of Agentic AI application, in an “AI factory” fashion, on Arm-based architectures with Ampere.
The Wallaroo AI Control Plane is comprised of LLM operations capabilities encompassing automated AI inference microservices packaging & deployment, ongoing model management with full governance, and observability.
With Wallaroo, enterprises can overcome production challenges mentioned above to easily operationalize LLMs for agentic AI applications and put in place measures such as RAG (Retrieval-Augmented Generation), complemented by Wallaroo’s LLM Listeners, to help ensure AI application efficacy, and the standards of security, privacy, compliance are avoiding outputs with toxicity, hallucinations etc.
RAG is a widely adopted method for tailoring LLM outputs with proprietary data. For LLM output validation with RAG, Wallaroo offers native capabilities for deploying and managing embedding models from sources such as HuggingFace directly on Ampere CPUs, eliminating complex hardware or infrastructure manipulations.
Through implementing RAG LLM as an authoritative source for models, it enhances the reliability of the Agentic AI application by ensuring that the generated outputs are relevant to the user’s context, and free from any potential hallucinations. Typically, organizations leverage pre-trained LLMs and enhance responses using pertinent business information, avoiding the need for extensive fine-tuning. AI engineers frequently employ RAG to improve model relevance in dynamic data landscapes due to its adaptability and straightforward implementation. A crucial initial step in implementing RAG is determining the vector embeddings strategy.
Embeddings offer a means of data representation by transforming input data, such as PDFs and text documents into numerical vectors that capture semantic relationships. These numerical vectors are then stored and retrieved in a vector database, and require a specialized AI model to generate the necessary embeddings for retrieval in the vector database. Embedding models are often small (less than 1 GB), making Arm-based architecture on Ampere CPU a cost and energy efficient infrastructure choice, to be combined with the Wallaroo control plane’s capability for packaging and orchestrating multi-step inference pipelines, which are critical for Agentic AI use cases.
RAG LLMs with Wallaroo for Agentic AI applications
While RAG is useful for ensuring context-appropriate agentic AI application, monitoring the health and reliability of Agentic AI applications and LLMs in production is crucial to maintain a tight feedback loop as application usage grows. To that effect, Wallaroo’s LLM ListenersTM offer a suite of tools to automatically scan Agentic AI application inferences for unwanted results, and enable AI developers to proactively address production issues
Fully compatible with Arm-based architecture, the Wallaroo LLM ListenersTM can be orchestrated to generate real-time monitoring reports and metrics for understanding how the LLM or the overall Agentic AI application is behaving and ensure that it is effective in production allowing AI teams to iterate quickly without impacting the bottom line.
Safeguard Agentic AI applications with Wallaroo’s LLM ListenerTM
Learn More: Monitoring & Iterating on custom LLMs for RAG-based applications on Arm with Wallaroo
“Wallaroo can natively enable deployment and management of AI models directly on Ampere, without any hardware or infrastructure manipulation. Additionally, Wallaroo’s AI inference automation capabilities and integrations toolkit allow seamless orchestration of AI inference microservices and Agentic services. As a result, AI developers building and deploying Agentic AI applications can ensure a tight feedback loop and continuously improve the quality of AI resulting in production with low operational burden.” – Younes Amar, VP of product & customer success at Wallaroo
Conclusion
The Wallaroo Universal AI Inference Platform optimized for Arm-based architecture on Ampere, signifies an ongoing commitment to empower enterprises in this drive for efficient, cost effective production AI and industrializing AI at scale in the cloud, on-premises, or at the edge.
By combining the strengths of Wallaroo.AI’s Universal AI Inference Platform and the high-performance, energy-efficient Arm-based architecture on Ampere, enterprises gain the means to quickly and efficiently operationalize AgenticAI applications at lower cost of ownership. This robust integration facilitates scaling AI deployment, governance, and observability, leveraging real-time data insights for faster decision-making, and driving unparalleled scale, agility, and efficiency for strategic AI initiatives.
For organizations eager to enhance their AI capabilities, Wallaroo.AI offers a powerful, simplified solution for deploying, managing, and optimizing AI models effectively.
To explore Wallaroo.AI’s offerings and streamline your AI deployment on OCI, visit Wallaroo.AI on OCI Marketplace or contact us to learn more.
