Cover Image

GPU 404: Not Found

Sep 1, 2023

How The AI Industry Is Plagued By An Acute GPU Shortage, Bringing Even The Tech Conglomerates To A Standstill

Imagine you're a passionate AI researcher, excited about training your groundbreaking language model. You've put in months of work, carefully crafting your model's architecture and fine-tuning its performance. But there's a catch – you can't find the GPUs you need to power your dreams. This has become a harsh reality for AI practitioners worldwide, as a severe GPU shortage is putting even the biggest tech giants on pause.

Microsoft recently updated a risk factor with potential disruption of services in its annual report with a reference to the importance of securing graphics processing units for its data centers.

"Sam Altman, CEO of OpenAI, recently announced that OpenAI is GPU-limited, leading to delays in their short-term plans such as fine-tuning, dedicated capacity, multimodality, etc."

“Who’s getting how many H100s and when is top gossip of the valley rn.”, said Andrej Karpathy, Former Director of AI @Tesla. 

Elon Musk scandalously highlighted the global GPU shortage by saying “GPUs are at this point considerably harder to get than drugs”.

Simply put, the availability of large-scale H100 and A100 GPU clusters is at an all-time low. Big cloud providers like AWS, Azure and Google Cloud are imposing strict quotas on their availability, while small-scale providers like the Nvidia-backed Coreweave, Vast.ai, RunPod or LambdaLabs rarely have instances with multiple H100/A100 GPUs available. 

Why exactly are these GPUs so much in demand? Because LLM development requires the latest, high-end GPUs with expansive VRAM for training and inference. Currently H100s and A100s, both offering 40Gb and 80Gb VRAM options with Nvidia’s latest state-of-the-art interconnect technologies are the prime contenders for this job, hence their extremely high demand and popularity.

Predictions indicate a 750x growth in AI compute demand compared to a mere 12x hardware improvement over the next 5 years. This underscores the significant impact of the severe GPU shortage on full-scale AI development and service availability.

Tracing the Origins of the Global GPU Shortage

The industry's GPU shortage is driven by multiple factors. 

  • The launch of OpenAI's ChatGPT and subsequent models like GPT-4.0 sparked soaring GPU demand. Meta, Google, and others soon joined suite after OpenAI's success, releasing their own implementations of large language models (LLMs) – LLaMA, Bard, Falcon, etc., further increasing the demand for new-gen industry-grade GPUs. Recent statistics reveal that the demand for GPUs has surged by 400% since the launch of OpenAI's ChatGPT, propelling the industry into uncharted territory.

  • Cryptocurrency mining, particularly for blockchain-based currencies like Bitcoin and Ethereum, has been another major culprit in further straining GPU supply, with miners vying for GPUs.


  • While the pandemic boosted AI research, it also caused significant disruptions in hardware production due to supply chain issues.


  • Additionally, the democratization of AI via accessible learning resources empowered more people to enter AI development, with most organizations and new-generation startups relying on GPU-powered cloud services.

The impact of these factors as we discussed is that there is a severe shortage of H100s and A100s in the industry. If you are reading this blog, your organization most likely already has quota issues, and you’re not able to get enough GPUs.

So What’s The Solution?

Obviously, Buy Your Own Hardware!

Sounds logical, but also not that easy. It’s expensive– A single Nvidia A100 80GB card is selling upwards of $15K on the white and gray markets. H100s are even pricier, selling upwards of $40K. Not to mention that these GPUs are so short in supply that they are rarely available on the white market from Nvidia or its resellers.

Finally, on top of this exorbitant pricing, you bear the headache of building and managing the hardware infrastructure.

Use Multiple Clouds

A more practical solution is the multi-cloud strategy, where various cloud providers are utilized for executing compute workloads. This approach, spanning tier-1 providers like AWS, Azure, Google Cloud, and tier-2 options such as DataCrunch, LambdaLabs, RunPod, CoreWeave, etc., expands access to a wider pool of available GPUs for data scientists for their AI tasks.

GPU availability delays can hinder productivity and research. The multi-cloud method enables simultaneous GPU use across platforms, eliminating wait times and promoting faster experimentation. Moreover, relying on one cloud can escalate costs. Multi-cloud adoption allows for diverse pricing models, including spot instances and discounts, resulting in cost-efficient GPU utilization.

By shelling out enough money, you can build your own multi-GPU H100/A100 server. Alternatively, a more budget-friendly approach involves renting cloud GPU VM instances through the multi-cloud strategy. While this might solve your GPU availability issue, one major problem still persists– Managing your training infrastructure.

Use Multi-Cloud Automation Tools To The Rescue

While the multi-cloud strategy appears promising for Generative AI and LLM developers, the setup and management of infrastructure across multiple clouds can be both tedious and costly.

Provisioning/deprovisioning VMs across clouds, managing VM states, configuration of data and network pipelines and handling instance failures are all DLOps tasks, often an overhead to the data scientists. What if there was a tool that automates all these tasks for them, so they can focus on the primary task at hand– training the models?

ScaleGenAI is a multi-cloud automation platform that empowers LLM and Generative AI developers to access an extensive network of GPUs and computing resources offered by various cloud providers. This abundance of computational power accelerates training time, enabling researchers to experiment with complex models and large datasets more efficiently.

ScaleGenAI Benefits:

  • Centralized Multi-Cloud Management: ScaleGenAI provides a unified interface to start jobs across all available clouds, simplifying the process of orchestrating AI workloads. The jobs can be started via CLI, UI or API with zero-code changes. The platform provides automatic search for the available cloud, cluster setup, tear-down and clean-up automations.

  • Data Accessibility and Security: ScaleGenAI ensures that data is accessible across all selected clouds with its unique Virtual Mount technology. You can stream your data directly from a variety of local or remote data stores: Amazon S3, Azure Blob Storage, Google File Store, SFTP, HTTP/HTTPS, Google Drive, Dropbox, etc. 

The data pipeline is E2E encrypted, and with a direct connection between your data store and virtual machine, ensuring zero data flow outside your infrastructure. Securely stream data to the selected cloud, with built-in on-VM caching to eliminate egress charge.

Cost optimization: ScaleGenAI can automatically detect failure and restart jobs on the spot-instances, which can significantly save on cloud bills. On average, spot instances are 3x cheaper than on-demand or reserved instances, leveraging which can cut your cloud spends by 80%.

Conclusion

The multi-cloud strategy is a game-changer for AI practitioners, seeking to accelerate AI training by harnessing the power of the high-demand, latest-gen Nvidia H100/A100 GPUs. The benefits– increased GPU availability, reduced waiting times, cost optimization, higher reliability, no vendor lock-in, and infrastructure automation, make this approach a compelling option for AI researchers who work with LLMs, transformers, diffusion models, etc. ScaleGenAI is the one step solution for your LLM training.

Ready to revolutionize your AI journey? Explore ScaleGenAI's capabilities and discover how you can break free from GPU constraints. It's time to shape the future of AI, one multi-cloud step at a time.










Join our Newsletter.

Cover Image

GPU 404: Not Found

Sep 1, 2023

How The AI Industry Is Plagued By An Acute GPU Shortage, Bringing Even The Tech Conglomerates To A Standstill

Imagine you're a passionate AI researcher, excited about training your groundbreaking language model. You've put in months of work, carefully crafting your model's architecture and fine-tuning its performance. But there's a catch – you can't find the GPUs you need to power your dreams. This has become a harsh reality for AI practitioners worldwide, as a severe GPU shortage is putting even the biggest tech giants on pause.

Microsoft recently updated a risk factor with potential disruption of services in its annual report with a reference to the importance of securing graphics processing units for its data centers.

"Sam Altman, CEO of OpenAI, recently announced that OpenAI is GPU-limited, leading to delays in their short-term plans such as fine-tuning, dedicated capacity, multimodality, etc."

“Who’s getting how many H100s and when is top gossip of the valley rn.”, said Andrej Karpathy, Former Director of AI @Tesla. 

Elon Musk scandalously highlighted the global GPU shortage by saying “GPUs are at this point considerably harder to get than drugs”.

Simply put, the availability of large-scale H100 and A100 GPU clusters is at an all-time low. Big cloud providers like AWS, Azure and Google Cloud are imposing strict quotas on their availability, while small-scale providers like the Nvidia-backed Coreweave, Vast.ai, RunPod or LambdaLabs rarely have instances with multiple H100/A100 GPUs available. 

Why exactly are these GPUs so much in demand? Because LLM development requires the latest, high-end GPUs with expansive VRAM for training and inference. Currently H100s and A100s, both offering 40Gb and 80Gb VRAM options with Nvidia’s latest state-of-the-art interconnect technologies are the prime contenders for this job, hence their extremely high demand and popularity.

Predictions indicate a 750x growth in AI compute demand compared to a mere 12x hardware improvement over the next 5 years. This underscores the significant impact of the severe GPU shortage on full-scale AI development and service availability.

Tracing the Origins of the Global GPU Shortage

The industry's GPU shortage is driven by multiple factors. 

  • The launch of OpenAI's ChatGPT and subsequent models like GPT-4.0 sparked soaring GPU demand. Meta, Google, and others soon joined suite after OpenAI's success, releasing their own implementations of large language models (LLMs) – LLaMA, Bard, Falcon, etc., further increasing the demand for new-gen industry-grade GPUs. Recent statistics reveal that the demand for GPUs has surged by 400% since the launch of OpenAI's ChatGPT, propelling the industry into uncharted territory.

  • Cryptocurrency mining, particularly for blockchain-based currencies like Bitcoin and Ethereum, has been another major culprit in further straining GPU supply, with miners vying for GPUs.


  • While the pandemic boosted AI research, it also caused significant disruptions in hardware production due to supply chain issues.


  • Additionally, the democratization of AI via accessible learning resources empowered more people to enter AI development, with most organizations and new-generation startups relying on GPU-powered cloud services.

The impact of these factors as we discussed is that there is a severe shortage of H100s and A100s in the industry. If you are reading this blog, your organization most likely already has quota issues, and you’re not able to get enough GPUs.

So What’s The Solution?

Obviously, Buy Your Own Hardware!

Sounds logical, but also not that easy. It’s expensive– A single Nvidia A100 80GB card is selling upwards of $15K on the white and gray markets. H100s are even pricier, selling upwards of $40K. Not to mention that these GPUs are so short in supply that they are rarely available on the white market from Nvidia or its resellers.

Finally, on top of this exorbitant pricing, you bear the headache of building and managing the hardware infrastructure.

Use Multiple Clouds

A more practical solution is the multi-cloud strategy, where various cloud providers are utilized for executing compute workloads. This approach, spanning tier-1 providers like AWS, Azure, Google Cloud, and tier-2 options such as DataCrunch, LambdaLabs, RunPod, CoreWeave, etc., expands access to a wider pool of available GPUs for data scientists for their AI tasks.

GPU availability delays can hinder productivity and research. The multi-cloud method enables simultaneous GPU use across platforms, eliminating wait times and promoting faster experimentation. Moreover, relying on one cloud can escalate costs. Multi-cloud adoption allows for diverse pricing models, including spot instances and discounts, resulting in cost-efficient GPU utilization.

By shelling out enough money, you can build your own multi-GPU H100/A100 server. Alternatively, a more budget-friendly approach involves renting cloud GPU VM instances through the multi-cloud strategy. While this might solve your GPU availability issue, one major problem still persists– Managing your training infrastructure.

Use Multi-Cloud Automation Tools To The Rescue

While the multi-cloud strategy appears promising for Generative AI and LLM developers, the setup and management of infrastructure across multiple clouds can be both tedious and costly.

Provisioning/deprovisioning VMs across clouds, managing VM states, configuration of data and network pipelines and handling instance failures are all DLOps tasks, often an overhead to the data scientists. What if there was a tool that automates all these tasks for them, so they can focus on the primary task at hand– training the models?

ScaleGenAI is a multi-cloud automation platform that empowers LLM and Generative AI developers to access an extensive network of GPUs and computing resources offered by various cloud providers. This abundance of computational power accelerates training time, enabling researchers to experiment with complex models and large datasets more efficiently.

ScaleGenAI Benefits:

  • Centralized Multi-Cloud Management: ScaleGenAI provides a unified interface to start jobs across all available clouds, simplifying the process of orchestrating AI workloads. The jobs can be started via CLI, UI or API with zero-code changes. The platform provides automatic search for the available cloud, cluster setup, tear-down and clean-up automations.

  • Data Accessibility and Security: ScaleGenAI ensures that data is accessible across all selected clouds with its unique Virtual Mount technology. You can stream your data directly from a variety of local or remote data stores: Amazon S3, Azure Blob Storage, Google File Store, SFTP, HTTP/HTTPS, Google Drive, Dropbox, etc. 

The data pipeline is E2E encrypted, and with a direct connection between your data store and virtual machine, ensuring zero data flow outside your infrastructure. Securely stream data to the selected cloud, with built-in on-VM caching to eliminate egress charge.

Cost optimization: ScaleGenAI can automatically detect failure and restart jobs on the spot-instances, which can significantly save on cloud bills. On average, spot instances are 3x cheaper than on-demand or reserved instances, leveraging which can cut your cloud spends by 80%.

Conclusion

The multi-cloud strategy is a game-changer for AI practitioners, seeking to accelerate AI training by harnessing the power of the high-demand, latest-gen Nvidia H100/A100 GPUs. The benefits– increased GPU availability, reduced waiting times, cost optimization, higher reliability, no vendor lock-in, and infrastructure automation, make this approach a compelling option for AI researchers who work with LLMs, transformers, diffusion models, etc. ScaleGenAI is the one step solution for your LLM training.

Ready to revolutionize your AI journey? Explore ScaleGenAI's capabilities and discover how you can break free from GPU constraints. It's time to shape the future of AI, one multi-cloud step at a time.










Join our Newsletter.