Word to the wise: Nvidia has a strong competitor now
For a long time, Nvidia has been recognized as the world’s largest producer of discrete GPUs that has recently experienced rapid growth due to the secular expansion of the PC gaming and data center markets.
Investors remain bullish on Nvidia after witnessing its incredible rise. In less than ten years, this company has rewarded stockholders with returns in excess of 2,500%. But the really smart ones know that now there is even a greater opportunity in this space – Nvidia’s competitor in the data center market.
Why has Nvidia been so successful with its GPUs? Because it’s now a requirement from consumers that mobile phones and computer systems have incredible processing abilities and graphics. Cultural trends such as gaming have also accelerated Nvidia’s meteoric rise. And while CPUs use scalar processing, i.e. one piece of data at a time, GPUs employ vector processing, handling a wide range of integers and floating-point numbers simultaneously.
CPUs can’t process machine learning and artificial intelligence tasks as efficiently as GPUs, so big data center operators have been installing more of Nvidia’s GPUs to handle those tasks.
But now Nvidia is challenged by another major player in this high-growth market – Graphcore, a startup developing chips and systems to accelerate AI workloads. Graphcore makes Intelligence Processing Units. The company believes that its IPU technology will become the worldwide standard for machine intelligence computing. The IPU’s unique architecture lets AI researchers undertake entirely new types of work, not possible using current technologies, to drive the next advances in machine intelligence.
Graphcore says that its graph processing technology is more efficient than both scalar and vector processing. The company’s IPU-POD16 server easily managed to outperform Nvidia’s DGX-A100 640GB server. Specifically, when systems were tested to train computer vision model RESNET-50, Graphcore’s unit did the job almost a minute faster. It took 28.3 minutes to train the model, while DGX100 took 29.1 minutes.
Nvidia is still much larger and more well-known than Graphcore, but the UK-based private company began advancing into North America, taking aim at Nvidia’s data center GPU business with its IPUs and purpose-built AI systems.
Graphcore has made it clear that it’s going after Nvidia’s fast-growing data center GPU business, claiming that its IPU architecture is better suited for AI applications because it was built from the ground up for “fine-grained parallelism” while also coming with 900 MB of on-board ultra-high-speed RAM, allowing the IPU to hold large AI models inside the memory. The company has also claimed that its M2000 systems provide more performance-per-dollar than Nvidia’s DGX systems.
“Graphcore is an amazing company that we are buying at Sakal Ventures. Artificial intelligence and machine learning will be revolutionary in the world. Let’s take the finance and trading sector, for example. Graphcore’s Intelligent Processing Unit is a completely new processing architecture designed for machine intelligence, capable of running advanced financial models up to 26x faster. It is no wonder that JP Morgan, D. E. Shaw & Co. and Citadel are among the company’s financial business customers,” says Kris Bort, founding partner of Sakal Ventures, a fund that focuses on late-stage private investing.
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