# Cerence Builds a Cost-Effective, High Throughput Bridge to The Cloud

**Customer:** Cerence

WEKA® NeuralMesh™ Helps Cerence Manage POSIX and Object Storage More Cost-Effectively – and Move Performance-Intensive Research Workloads to the Cloud.

## Results

- **900TB** of data managed on WEKA cluster
- **40** HPE Proliant DL360 servers with dual 25GbE networking
- **3+Petabytes** total data managed with automated tiering capability

Company Overview

## AI for a World in Motion

[Cerence](http://cerence.com/) is a multinational software company based in the U.S. that develops artificial intelligence (AI) assistant technology for the automobile industry. The company builds AI-powered speech recognition and language models that create a deep understanding of human behavior, culture, and languages to improve the in-car experience. Their mission is to help make automotive transport safer and human interaction with the car more natural and comfortable.

The Challenge

## Managing Mountains of Data Without a Monolithic Cost

In 2019, Cerence was spun out of its parent company, Nuance Communications, and became an independent company. Its compute infrastructure supports multiple performance-intensive workflows, including natural language processing (NLP), natural language understanding (NLU), text-to-speech, and pattern recognition applications like predictive keyboards. The company used a legacy Portable Operating System Interface (POSIX) file system for its research workflows and needed an efficient way to increase its performance while managing the mountains of data it was collecting cost-effectively. They wanted to avoid investing in an expensive, monolithic data infrastructure since it would be cost-prohibitive to run at scale and were interested in a hybrid cloud solution, so they began exploring alternatives.

## Solution Requirements

**The new solution needed:**

- **Cost efficiency:** Cost-effectively support Cerence’s POSIX file system & object storage environment
- **Hybrid cloud tiering:** Seamlessly tier data from on-premises to the public cloud
- **High performance:** Support high throughput storage transfer protocols such as NVMe
- **Scalability:** Flexibly scale with the growing needs of the business

**The Solution**

## **NeuralMesh Running on HPE Server**

Cerence selected NeuralMesh to manage 900TB of data and support NLP and NLU workloads on a cluster comprised of 40 HPE Proliant DL360 servers with dual 25GbE networking adapters interconnected with four switches for high availability, performance, and redundancy.

NeuralMesh’s two-tier architecture supports both NVMe flash and disk-based storage in a single data management solution. Cerence opted to implement NeuralMesh in a converged configuration running on the HPE servers to create a single namespace from the locally attached NVMe drives. Each server has one network connection, two NVMe drives dedicated to NeuralMesh, and one GPU card.

Today, Cerence’s IT team manages over 3 petabytes of data on SUSE object storage, with 900TB assigned to NeuralMesh, all running on a cluster of 9 HPE Apollo 4200 servers with 24 14TB drives. The team also uses an HPE Apollo 6500 server with 8 GPUs for multi-GPU processing.

![Cerence AI | AI for a World in Motion](https://cdn.sanity.io/images/ult5g8gw/production/ffa2e6b5f842817692e85be3b03df852eb33a471-800x560.webp)

![Cerence AI | AI for a World in Motion](https://cdn.sanity.io/images/ult5g8gw/production/ac24059acb350eb5a02efdcfc85c78637c0cf7ee-800x560.webp)

> We looked at our legacy architecture, and instead of taking an evolutionary approach, upgrading every component, we took the revolutionary approach. NeuralMesh cost-effectively enables the use of both POSIX and object storage with performance and latency that is far superior to any other solution.
>
> — Bridget Collins, Chief Information Officer • Cerence

Outcomes

## Significant Performance Improvement and Limitless Capacity Scaling

  
**With its new hybrid cloud solution, Cerence achieved:**

- Public cloud integration for computing elasticity
- Significant throughput and performance improvement
- Limitless capacity scaling
- Automated tiering
- Improved resource utilization

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