“We’re now prepared for a future where the amount of data will continue to grow exponentially from tens or hundreds of petabytes to exascale and beyond,” said Jensen Huang at the NVIDIA A100 launch in 2020. Did you know the NVIDIA A100 is 20x faster than its predecessor, powering breakthroughs across industries? It accelerated seismic imaging, slashed AI latency by over 3x, and reduced simulation times from weeks to days. With features like multi-instance GPU (MIG) and mixed precision computing, the NVIDIA A100 redefines performance for multiple use cases.
And we bet you didn't know about these real-world use cases of the NVIDIA A100.
Meta's Llama and Llama 2 are among the world's most advanced open-source AI models. Many don’t realise the staggering scale of infrastructure required to develop them. Meta used 16,000 NVIDIA A100 GPUs to train Llama and Llama 2, processing terabytes of data across multiple tasks to generate human-like responses. The NVIDIA A100 GPUs facilitated high-speed matrix computations essential for training language models, helping Meta release advanced models that beat proprietary ones like OpenAI’s GPT.
Thanks to the NVIDIA A100's energy efficiency and scalability, Meta effectively reduced training costs while maintaining top-tier performance [see source here]. Llama 2’s pretraining required 3.3 million GPU hours on the NVIDIA A100 80GB (with a TDP of 350-400W). Meta's sustainability initiatives fully offset the estimated carbon emissions of 539 metric tons of carbon dioxide equivalent.
Want to Get Started with Latest Llama Models? Explore our tutorials below:
Stability AI trained Stable Diffusion V2 on 256 NVIDIA A100 GPUs for 200,000 compute hours. The NVIDIA A100's exceptional tensor core performance and memory bandwidth were monumental in training Stable Diffusion to generate high-quality images through text prompting. This level of scalability allowed Stability AI to bring state-of-the-art generative AI tools to a global audience and now we have one of the world’s most advanced AI image generators.
Did you know? Stability AI recently released Stable Diffusion 3.5. If you haven’t already, try it now on Hyperstack. Follow our tutorial here to get started.
LILT, a company specialising in AI-powered language translation used NVIDIA A100 GPUs alongside the NeMo framework to create AI models capable of processing high volumes of multilingual content. When a European law enforcement agency required a fast and efficient solution for translating large volumes of content in low-resource languages under tight deadlines, they partnered with LILT.
And the result was astonishing! Using LLMs developed with NVIDIA A100 GPUs and NeMo, LILT enabled the agency to achieve translation speeds exceeding 150,000 words per minute. LILT’s platform delivers up to 30 times higher character throughput in inference performance compared to equivalent models running on CPUs.
Interesting Read: Content Creation with AI Series Part 1: Bringing Icons Back to Life
Perplexity AI leveraged NVIDIA A100 GPUs with TensorRT-LLM to significantly enhance the efficiency of its inference API. It achieved remarkable reductions in latency and operational costs. With NVIDIA A100 GPUs, Perplexity could manage substantial inference workloads to ensure consistent performance for LLMs at scale. It’s a great example of how NVIDIA’s A100 GPUs can deliver cost-effectiveness and high performance in large-scale generative AI deployments.
Get High-Speed Networking of up to 350Gbps with NVIDIA A100 for fast inference and ultra-low latency on Hyperstack.
Shell, an international energy company used NVIDIA A100 GPUs for high-performance computing (HPC) to process and analyse vast amounts of data in oil and gas exploration. The NVIDIA A100 GPUs allowed Shell to extract actionable insights from complex datasets, improving computational efficiency across various applications, including seismic imaging and reservoir simulation. By adopting NVIDIA A100 GPUs, Shell reduced the time required for simulations and data processing for faster decision-making and enhancing operational efficiency. This example shows the NVIDIA A100's adaptability in handling various computational challenges beyond AI.
“The NVIDIA A100 Tensor Core GPUs are fast and reliable for tracking individual biomass particles in the reactors’ fluid flow simulations”
Piet Moeleker, general manager of fluid flow and reactor engineering
For training advanced AI models or optimising AI workflows, the NVIDIA A100 is an excellent choice. If you're looking for a cost-effective option, the NVIDIA RTX A6000 on Hyperstack is worth considering. Check out our blog for a detailed comparison of the NVIDIA A100 and NVIDIA RTX A6000 to help you choose the ideal GPU for your projects.
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The NVIDIA A100 is a high-performance GPU designed for AI, deep learning, and high-performance computing. It offers up to 20x performance improvement over its predecessor, the V100.
The key features of the NVIDIA A100 include Tensor Cores for deep learning, multi-instance GPU (MIG) for efficient workload management, and enhanced memory bandwidth for handling large datasets.
The NVIDIA A100 excels at high-speed matrix calculations and provides remarkable scalability, making it ideal for training large AI models and performing fast inference.
You can access the NVIDIA A100 GPU easily on Hyperstack. Simply sign up here: https://console.hyperstack.cloud/ and start deploying at competitive pricing, with access starting from just $0.98/hour.
The NVIDIA A100 benefits industries such as AI, healthcare, energy and finance, helping them accelerate research, improve decision-making and optimise operational efficiency.