Compute is key to further AI advancements
To achieve higher levels of compute for AI and deep learning, particularly in tasks like video generation akin to Sora's capabilities, it's crucial to understand the current landscape of computational resources and what's needed for future advancements.
Currently
As of now, the foundation of AI acceleration heavily relies on GPUs due to their ability to handle parallel processing tasks efficiently. Modern GPUs, particularly those designed for AI workloads, boast thousands of cores. This architecture differs significantly from CPUs, which may have up to a few dozen cores, making GPUs vastly superior for parallel computing tasks required in deep learning and AI.
NVIDIA has been at the forefront of this acceleration, with its latest GPUs delivering significant improvements in AI inference speeds—up to a 1000x increase over the last decade. This has been facilitated by advancements in Tensor Cores, which are specifically designed for the matrix math that underpins neural network computations, and by software ecosystems that support AI development, such as NVIDIA AI Workbench and TensorRT.
However, the complexity of AI models is growing exponentially, with state-of-the-art models like GPT-4 now exceeding a trillion parameters. This growth outpaces Moore's Law, requiring a 10x increase in computational capability annually to keep up. To address this, systems have scaled up to supercomputers, utilizing technologies like NVLink and NVIDIA Quantum InfiniBand networks to connect GPUs, achieving unprecedented levels of shared memory and AI performance.
For instance, NVIDIA's DGX GH200, a large-memory AI supercomputer, can combine up to 256 NVIDIA GH200 Grace Hopper Superchips into a single GPU system with 144 terabytes of shared memory, offering four petaflops of AI performance per superchip.
Looking ahead
Looking ahead, to move from current capabilities to even higher levels of compute that could support video generation technologies like Sora at a more extensive and accessible scale, a few measurable milestones include:
Further advancements in GPU architecture to increase the number of cores and efficiency of Tensor Cores, providing greater parallel processing capabilities for AI workloads.
Development of AI-specific chipsets that could potentially offer better performance or efficiency for particular types of AI computations, such as video processing or generative models.
Increased availability and affordability of high-performance computing (HPC) resources for developers and researchers, possibly through cloud-based platforms, to democratize access to the necessary computational power.
Optimization of AI software and algorithms to better leverage existing hardware capabilities, reducing the computational overhead for training and inference tasks.
These advancements would necessitate a collaborative effort across the semiconductor industry, AI research community, and cloud service providers to ensure the necessary computational resources are available and accessible for pushing the boundaries of AI and deep learning further.
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