Skip to main content
  1. Home
  2. Computing
  3. News

Amazon and Nvidia bring artificial intelligence to the cloud with T4 GPUs

By
Add as a preferred source on Google
Nvidia T4 Enterprise Server Wall
Image used with permission by copyright holder

Artificial intelligence and machine learning aren’t new concepts to the world of cloud computing, but Nvidia and Amazon are aiming to take it to the next level. Nvidia has announced that mainstream servers designed to run the company’s data science acceleration software are now available; additionally, Amazon will be implementing the technology into its Amazon Web Services (AWS) stack for customers looking to take advantage of accelerated machine learning tasks in the cloud.

The new servers feature Nvidia’s T4 GPUs running on the company’s Turing GPU architecture; this raw hardware power combined with Nvidia’s CUDA-X A.I. libraries will enable businesses and organizations to more efficiently handle A.I.-based tasks, machine learning, data analytics, and virtual desktops. Designed for the data center the T4 GPUs draw only 70 watts of power during operation. Companies offering the new servers include Cisco, Dell EMC, Fujitsu, HP Enterprise, Inspur, Lenovo, and Sugon.

Recommended Videos

For businesses interested in the deployment of Nvidia T4 GPUs on AWS, Amazon announced that the instances will be available through the Elastic Compute Cloud. Through the AWS Marketplace, customers will be able to pair G4 instances with Nvidia’s GPU acceleration software. Additionally, Amazon will be supported by the company’s Elastic Container Service for Kubernetes, allowing for easy scalability depending on the required task.

Nvidia T4 GPU
Image used with permission by copyright holder

According to Matt Garman, vice president of Compute Services at AWS, the two companies “have worked together for a long time to help customers run compute-intensive A.I. workloads in the cloud and create incredible new A.I. solutions.” The introduction of Nvidia T4 GPUs into the company’s offers will is said to make “it even easier and more cost-effective for customers to accelerate their machine learning inference and graphics-intensive applications.”

Every new T4 server introduced by Cisco, Dell EMC, Fujitsu, HP Enterprise, Inspur, Lenovo, and Sugon will also be Nvidia NGC-Ready validated; this program designed by Nvidia is awarded to servers which demonstrate that they can excel in a full range of different accelerated workloads. Recently, Intel teamed up with Facebook to develop CPUs for machine learning tasks, now Nvidia’s solution ensures that the GPU half of the equation isn’t left behind.

Michael Archambault
Michael Archambault
Former Digital Trends Contributor
Michael Archambault is a technology writer and digital marketer located in Long Island, New York. For the past decade…
Topics
The Mac Pro nearly received an M3 Extreme chip twice as powerful as M3 Ultra
High production costs likely killed Apple’s M3 Extreme plans
Apple's Mac Pro on a table at a press event.

Apple discontinued the Mac Pro earlier this year, ending a 20-year run for a computer that once represented the very best of the company’s desktop lineup. However, Apple reportedly had much bigger plans for the machine before ultimately replacing it with the Mac Studio.

According to Bloomberg’s Mark Gurman, Apple developed an M3 Extreme chip that could have offered twice as many CPU and GPU cores as the M3 Ultra. The processor was intended to sit above the Ultra tier and could have finally given the Mac Pro the performance advantage it badly needed. Apple eventually abandoned the chip due to concerns over production costs and limited demand for such an expensive machine.

Read more
Hidden prompts can secretly rewrite an AI’s memory, and researchers say that’s a serious problem
Researchers discover AI attack that rewrites an assistant's long-term memory
Chatbot on a smartphone.

Large language models are getting better at remembering us. Whether it's your preferred writing style, recurring tasks, shopping habits or project deadlines, AI assistants are increasingly storing long-term memories to make future conversations feel more personal and useful. But according to new research, that same feature could become one of AI's biggest security vulnerabilities.

Researchers from New Mexico State University have demonstrated a new attack called GhostWriter, capable of secretly planting false memories inside AI agents. Rather than stealing information outright, the attack manipulates what an AI remembers, potentially causing it to make dangerous decisions long after the original attack has taken place.

Read more
This experiment shows how easy it is to poison an open-weight AI model for under $100
This research raises new doubts about trusting open weight AI models.
Computer, Electronics, Laptop

Open-weight AI models have been having a moment lately. Just this month, Moonshot's massive Kimi K3 model landed close behind Claude Fable 5 and GPT 5.6 Sol in several benchmarks, all while remaining fully open-weight and downloadable by anyone.

However, Katie Paxton-Fear, a cybersecurity lecturer at Manchester Metropolitan University and staff security advocate at Semgrep, managed to poison an open-weight model and proved how easily that openness can be turned against you (via The Register).

Read more