Live the Dream of Unlimited Compute Power


Live the Dream of Unlimited Compute Power

 

This startup aims to speed up your deep learning initiatives, simply by optimizing compute.

 

We hear a lot about the power of AI to potentially change our lives, but also about its challenges – cost, difficulty getting to production, complexity for companies to deploy, the dearth of data scientists. Add to that the things that are critical for building an effective AI datacenter: fast storage for managing huge datasets, efficient networking, and state-of-the-art hardware accelerators. Bottom line: if your storage isn’t vast and your network isn’t fast, your innovation and progress is going to be severely limited.

Solution: easier access to — and control of — hardware accelerators such as graphics processing units (GPUs). Run:AI’s reason for being is to greatly increase compute resource access to data scientists, essentially creating unlimited compute. As researchers access more compute, utilization across the AI datacenter can be increased as much as three times, from 25 percent to more than 75 percent.

Ultimately, Run:AI offers a deep-learning orchestration platform that helps organizations manage the resource allocation of GPUs while increasing cluster utilization.

As a result, IT departments can gain full control over GPU utilization across nodes, clusters and sites, while data scientists can speed up initiatives by accessing compute when and how they need it.  

If your company is new to the AI space, as many are, ask yourself these questions:

  • How am I managing my company’s compute power?
  • How do IT teams allocate GPU resources? How do data scientists share these resources easily and efficiently?

Many companies currently keep track of GPU usage the old fashioned way — Excel spreadsheets, for instance. In the meantime, when it comes to a GPU’s limited access, it’s often every data scientist team for themselves, calling dibs on a GPU server before anyone else can claim it. Not an easy, fast way to team up and innovate.

“At some point, data scientists will need massive computing power in order to build and train AI models,” says Run:AI CTO and co-founder Ronen Dar. “Getting access to GPUs can be difficult. The company will need to build infrastructure management and resource allocation for those data scientists, whether on-premises or in the cloud.”

Ronen Dar, CTO and Co-Founder, Run:AI

 

Run:AI works to reduce blind spots due to this static allocation of GPUs. The Run:AI graphic user interface (GUI) helps speed productivity by providing a more holistic view of: 

  • GPU infrastructure utilization
  • Usage patterns
  • Workload wait times
  • Related costs

These revelations often come as a surprise to Run:AI clients.

“Many companies think that their GPUs are being fully utilized,” says, Run:AI marketing VP Fara Hain, “but they have no idea there is low utilization. So we help by e optimizing their GPUs because they are  a very important resource for the company.” 

Run:AI pulls it all together with two coexisting strategies: utilization and speed:

  • Optimizing utilization of AI clusters by enabling flexible pooling and the sharing of resources between users and teams. Run:AI’s software distributes workloads in an “elastic” way: changing the number of resources allocated to a job as needed. The result: data science teams can run more experiments on the same hardware.
  • Maximum speed to run data experiments and training AI models (better utilization helps make this happen). Abstract AI workloads from compute power and then apply distributed computing principles for faster results — essentially allowing a guaranteed quota of GPUs for each project.

A recent funding round is allowing the Israel-based company to expand its global reach and hire more engineers. The goal, always, is to help data scientists manage and expand their workloads and help them train the models that lead to amazing breakthroughs.”

“If you open up compute power to very smart people, they will do incredible things,” Ronen says. “We’re just trying to prep the platform so that they can do that.”

Find out more about our content partner, Run:AI, and to apply for a free trial, click here.


Comments

Leave a Reply

Your email address will not be published. Required fields are marked *

Related Posts

Recent Posts

Artificial Intelligence in Healthcare: What it Can Achieve - Artificial Intelligence in Healthcare: What it Can Achieve Artificial intelligence (Machine Learning) in medicine is poised to revolutionize the way…
Live the Dream of Unlimited Compute Power - Live the Dream of Unlimited Compute Power   This startup aims to speed up your deep learning initiatives, simply by…
Comparison of Popular AI Frameworks - Comparison of Popular AI Frameworks   Introduction "A computer program is said to learn from experience E with respect to…
Here’s The Main Reason Why Most AI Projects Fail - How’s your AI project coming along? If training data challenges are getting in the way of your goals, it may…
How AI is Transforming Telehealth - As technology continues to advance, artificial intelligence (AI) has become an everyday reality. And one industry it is rapidly transforming…
The Best States for New Businesses in the AI Space - The Best States for New Businesses in the AI Space The growth of AI businesses is becoming explosive. While the…
How Levatas Teaches Spot New Tricks -   One of the key components to Levatas’ success: partnerships. Customers looking for accurate analog gauge reading and thermal heat…
How AI is Revolutionizing Education -   Artificial intelligence has become increasingly relevant in a number of major industries. We read a lot about how it’s…

Popular Posts

Does Healthcare AI Meet Basic Ethics Principles? - Ingrid Vasiliu-Feltes Chief Quality and Innovation Officer MEDNAX, Health Solutions Partner Over the past decade we have noticed an exponential…
Machine Learning and Artificial Intelligence in Banking - Artit "Art" Wangperawong Distinguished Engineer US Bank Introduction Every company’s AI journey is different. We’re all trying to figure out…
Machine Learning for Pricing and Inventory Optimization @ Macy’s - Jolene Mork Senior Data Scientist Macy's Iain Stitt Data Scientist Macy's Bhagyesh Phanse VP, Data Science Macy's Overview In this…
Artificial Intelligence & Cybersecurity: Math Not Magic - Wayne Chung CTO FBI Introduction The field of cybersecurity has slowly progressed from an art to a science. It has…
AI/ML in Investment and Risk Management: Recent Applications, Use Cases, and Implementation Challenges - Arvind Rajan Managing Director - Head of Global & Macro PGIM Fixed Income Introduction Investing is a completely different ballgame…
Top AI Conferences - Interested in learning the latest in AI this year? We’ve compiled a list of the top artificial intelligence conferences in…
Machine Learning in Production: From Research to the Customer - Ameen Kazerouni Lead Data Scientist Zappos Overview In this presentation Ameen Kazerouni, the Lead Data Scientist at Zappos, walks through…
How COVID-19 is Impacting the State of AI in Banking - On this panel, industry experts (listed above) discussed The State of AI in Banking and how COVID-19 is affecting it.…
“Ask Me Anything” with Zappos’s Head of AI/ML Research & Platforms, Ameen Kazerouni - Ameen Kazerouni Head of AI/ML Research & Platforms Zappos Family of Companies Ai4 recently hosted an "Ask Me Anything" session…
The Autonomous Pharmacy: Applying AI and ML to Medication Management Across the Care Continuum - Ken Perez VP of Healthcare Policy Omnicell, Inc. Ken applies artificial intelligence (AI) and machine learning (ML) solutions to medication…