A new fine-tuning performance benchmark for BridgeTower, a Vision-Language (VL) AI model, has shown that there’s life to the AI acceleration camp other than Nvidia’s green. While Nvidia does dominate the AI acceleration market (through exceptional foresight, a well-thought-out and documented software stack, and pure processing performance), other players are keen to take a piece of the AI market for themselves. And at least for BridgeTower, Intel’s own Gaudi 2 silicon (designed and fabricated through Intel’s $2 billion, 2019 acquisition of Habana) has been shown by Hugging Face to outperform Nvidia’s A100 80 GB by a staggering 2.5x – and it even beats Nvidia’s prodigy-child H100 by 1.4x.

According to Habana, the momentous speedups are the result of a hardware-accelerated data-loading system – one of the bottlenecks for AI model fine-tuning, and especially-so for VL models. Loading a workload into memory is often one a performance bottleneck wherever computing lies, so it’s not that out of the left-field that Habana would look to optimize this particular step in the training process. The main bottleneck relates to how CPUs get hamered with many costly operations such as image decoding and image augmentation (a similar issue to the GPU draw-call debate), which lead the HPU (or Nvidia GPU) to stall while waiting for further data to be processed (by the CPU) and then sent over to the AI accelerator of choice.

Full story : Intel Habana Gaudi Beats Nvidia’s H100 in Visual-Language AI Models: Hugging Face.

Additional Context/Related Research

Technology Convergence and Market Disruption: Rapid advancements in technology are changing market dynamics and user expectations. See: Disruptive and Exponential Technologies.

AI Discipline Interdependence: There are concerns about uncontrolled AI growth, with many experts calling for robust AI governance. Both positive and negative impacts of AI need assessment. See: Using AI for Competitive Advantage in Business.

Benefits of Automation and New Technology: Automation, AI, robotics, and Robotic Process Automation are improving business efficiency. New sensors, especially quantum ones, are revolutionizing sectors like healthcare and national security. Advanced WiFi, cellular, and space-based communication technologies are enhancing distributed work capabilities. See: Advanced Automation and New Technologies

Emerging NLP Approaches: While Big Data remains vital, there’s a growing need for efficient small data analysis, especially with potential chip shortages. Cost reductions in training AI models offer promising prospects for business disruptions. Breakthroughs in unsupervised learning could be especially transformative. See: What Leaders Should Know About NLP

Computer Chip Supply Chain Vulnerabilities: Chip shortages have already disrupted various industries. The geopolitical aspect of the chip supply chain necessitates comprehensive strategic planning and risk mitigation. See: Chip Stratigame

Rise of the Metaverse: The Metaverse, an immersive digital universe, is expected to reshape internet interactions, education, social networking, and entertainment. See Future of the Metaverse.

Bitcoin’s Momentum: Bitcoin seems unstoppable due to solid mathematical foundations and widespread societal acceptance. Other cryptocurrencies like Ethereum also gain prominence. The Metaverse’s rise is closely tied to Ethereum’s universal trust layer. See: Guide to Crypto Revolution

Materials Science Revolution: Room-temperature ambient pressure superconductors represent a significant innovation. Sustainability gets a boost with reprocessable materials. Energy storage sees innovations in solid-state batteries and advanced supercapacitors. Smart textiles pave the way for health-monitoring and self-healing fabrics. 3D printing materials promise disruptions in various sectors. Perovskites offer versatile applications, from solar power to quantum computing. See: Materials Science