AI Chips in 2025: The end of “more GPUs is all you need”?
It’s early 2025, and we may already be witnessing a redefining moment for AI as we’ve come to know it in the last couple of years. Is the canon of “more GPUs is all you need” about to change? By George Anadiotis What an unusual turn of events. First, the Stargate Project. The joint venture […]
Read More →The future of AI chips: Leaders, dark horses and rising stars
The future of AI chips is about more than NVIDIA: AMD, Intel, chiplets, upstarts, analog AI, optical computing, and AI chips designed by AI. By George Anadiotis The interest and investment in AI is skyrocketing, and generative AI is fueling it. Over one-third of CxOs have reportedly already embraced GenAI in their operations, with nearly half […]
Read More →AI Chips in 2024: NVIDIA, MLPerf benchmarks, Huang’s law, and competition
What we learned on AI Chips in 2024 by keeping track of NVIDIA’s latest announcements, talking to industry experts, and scanning news and analyses By George Anadiotis Exploring AI chips has been a pastime, as a well as a popular theme in Orchestrate all the Things articles. In 2023, we felt like we fell somewhat […]
Read More →NVIDIA unveils Hopper, its new hardware architecture to transform data centers into AI factories
NVIDIA just announced Hopper, a new GPU architecture that promises significant performance improvements for AI workloads. We look under the hood to decipher whether the emphasis on Transformer AI models translates to a radical redesign, and look at the updates in the software stack.
Read More →Running AI workloads is coming to a virtual machine near you, powered by GPUs and Kubernetes
Run:AI offers a virtualization layer for AI, aiming to facilitate AI infrastructure. It's seeing lots of traction and just raised a $75M Series C funding round. Here's how the evolution of the AI landscape has shaped its growth.
Read More →Bringing Deep Learning to your hardware of choice, one DeciNet at a time
Training deep learning models is costly and hard, but not as much as deploying and running them in production. Deci wants to help address that.
Read More →What will applied AI look like in 2022?
AI adoption has skyrocketed throughout the last 18 months. Besides Joe McKendrick, who wrote the foundational piece on HBR, professionals who work on AI would readily attest to this statement. Google search seems to be in on this not-so-secret too: When prompted with “AI adoption,” its auto-complete spurts out “skyrocketed over the last 18 months”.
Read More →SambaNova is enabling disruption in the enterprise with AI language models, computer vision, recommendations, and graphs
SambaNova just added another offering under its umbrella of AI-as-a-service portfolio for enterprises: GPT language models. As the company continues to execute on its vision, we caught up with CEO Rodrigo Liang to look both at the big picture and under the hood.
Read More →Nvidia doubles down on AI language models and inference as a substrate for the Metaverse, in data centers, the cloud and at the edge
At Nvidia's GTC event today, CEO Jensen Huang made announcements the company claims have the potential to transform multi-trillion dollar industries. We cherry-pick from the announcements, focusing on the hardware and software infrastructure that powers the applications that make the headlines.
Read More →Machine learning at the edge: A hardware and software ecosystem
Being able to deploy machine learning applications at the edge bears the promise of unlocking a multi-billion dollar market. For that to happen, hardware and software must work in tandem. Arm's partner ecosystem exemplifies this, with hardware and software vendors like Alif and Neuton working together.
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