Meta’s MTIA? Best illustration that succeeding with an in-house chip is challenging…
Meta announced last week MTIA v1, its first-generation AI inference accelerator. As a follow-up to the work we published yesterday comparing cluster economics of Nvidia GPU vs. Google TPU, we now take a closer look at MTIA and how it compares to GPUs and other A.I. chips./research/the-cluster-economics-of-nvidia-gpu-vs-google-tpuhow-different-implications-for-market-structure/
MTIA is a good illustration of the challenges associated with developing in-house chips.
Facebook is building and engaging products that enable people to connect and share with friends and family through mobile devices, personal computers, virtual reality headsets, and in-home devices. The company's products include: Facebook, which enables people to connect, share, discover, and communicate with each other on mobile devices and personal computers; Instagram, which is a place where people can express themselves through photos, videos, and private messaging, and explore their interests in businesses, creators and communities; Messenger and WhatsApp, which are messaging applications; and Oculus, which connects people through its Oculus virtual reality products.
NVIDIA engages in graphics processing unit (GPU)-based visual computing and accelerated computing platforms. The company has two segments, GPU and Tegra Processor, which are based on a single underlying architecture. The company's GPU product brands are aimed at markets including GeForce for gamers; Quadro for designers; Tesla and DGX for artificial intelligence data scientists and big data researchers; and GRID for cloud-based visual computing users. The company's Tegra brand integrates an entire computer onto a single chip, and incorporates GPUs and multi-core CPUs to drive supercomputing for autonomous robots, drones, and cars, as well as for game consoles and mobile gaming and entertainment devices.
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