Sign up to save your podcasts
Or
Share The H100 GPU Bubble Burst and the Future of AI
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
The H100 GPU Bubble Burst and the Future of AI
In this episode, we dive into the dramatic rise and fall of H100 GPU prices and what it means for the future of AI. Remember the time when trying to secure an H100 GPU was like hunting for El Dorado? At the height of the AI gold rush, these GPUs were in high demand, fetching up to $8 an hour. Companies big and small scrambled to get their hands on them, and GPU farms were cashing in. Fast forward to today, and rental prices have plummeted to $1–$2 an hour. But what caused this sudden collapse?
We break down the perfect storm of events that led to this massive price drop. From the rise of open-weight models to the introduction of more affordable GPUs like NVIDIA’s L40, fine-tuning techniques, and even the impact of the crypto crash, all these factors contributed to the shift in demand. The industry has moved from requiring massive clusters of GPUs to leveraging just one, thanks to advancements in AI model fine-tuning.
We’ll also explore how resellers like RunPod, SF Compute, and Vast.ai are capitalizing on unused GPU capacity, further driving down prices and democratizing access to AI computing power. What does this mean for the future of AI hardware? Will we see a split between high-end AI projects and more accessible, cost-effective setups for smaller developers?
As competition in the AI chip market heats up, with AMD and Intel entering the race, we’re witnessing a reshaping of the industry. While some companies may regret locking in expensive long-term GPU leases, there’s a silver lining: the lowering of hardware costs opens doors for more innovation and allows more people to participate in AI development.
Tune in as we discuss how this dramatic shift is more than just a blip on the radar—it’s a sign of how fast the tech world moves and what’s on the horizon. The H100 bubble may have burst, but the future of AI? It's more exciting and accessible than ever before. Join us for this insightful conversation about where AI is headed and how it's reshaping everything from the apps we use to the future of work.
FAQ:
Why have H100 GPU prices dropped?
H100 prices fell from $8/hour last year to $2/hour due to several factors:
What is the return on investment for H100?
Investing in new H100 units for rental is currently unprofitable. At $2.85/hour, returns barely exceed 10%. Below $1.65/hour, investments are likely to be unprofitable.
What rental models exist for H100?
There are two rental models:
How has the rise of Open-Source models affected the H100 market?
Open-Source models have increased demand for model refinement and inference, while reducing the need for H100 in training.
Why are large companies leaving public cloud platforms?
Large companies like Facebook and OpenAI build their own H100 clusters due to the need for larger clusters, accounting benefits, and the option to buy data centers.
What alternatives to H100 exist?
Other options include Nvidia L40S for inference, AMD MX300 and Intel Gaudi 3 for single-node tasks, and repurposed GPUs from cryptocurrency mining.
What are the consequences of falling H100 prices?
The price drop may cause segmentation, with large clusters costing more, and new investors potentially facing losses. It may also accelerate Open-Source model adoption.
Is it worth buying new H100 units?
In most cases, buying new H100 units is not recommended. Renting or using alternative GPUs is more cost-effective
View all episodes
By j15In this episode, we dive into the dramatic rise and fall of H100 GPU prices and what it means for the future of AI. Remember the time when trying to secure an H100 GPU was like hunting for El Dorado? At the height of the AI gold rush, these GPUs were in high demand, fetching up to $8 an hour. Companies big and small scrambled to get their hands on them, and GPU farms were cashing in. Fast forward to today, and rental prices have plummeted to $1–$2 an hour. But what caused this sudden collapse?
We break down the perfect storm of events that led to this massive price drop. From the rise of open-weight models to the introduction of more affordable GPUs like NVIDIA’s L40, fine-tuning techniques, and even the impact of the crypto crash, all these factors contributed to the shift in demand. The industry has moved from requiring massive clusters of GPUs to leveraging just one, thanks to advancements in AI model fine-tuning.
We’ll also explore how resellers like RunPod, SF Compute, and Vast.ai are capitalizing on unused GPU capacity, further driving down prices and democratizing access to AI computing power. What does this mean for the future of AI hardware? Will we see a split between high-end AI projects and more accessible, cost-effective setups for smaller developers?
As competition in the AI chip market heats up, with AMD and Intel entering the race, we’re witnessing a reshaping of the industry. While some companies may regret locking in expensive long-term GPU leases, there’s a silver lining: the lowering of hardware costs opens doors for more innovation and allows more people to participate in AI development.
Tune in as we discuss how this dramatic shift is more than just a blip on the radar—it’s a sign of how fast the tech world moves and what’s on the horizon. The H100 bubble may have burst, but the future of AI? It's more exciting and accessible than ever before. Join us for this insightful conversation about where AI is headed and how it's reshaping everything from the apps we use to the future of work.
FAQ:
Why have H100 GPU prices dropped?
H100 prices fell from $8/hour last year to $2/hour due to several factors:
What is the return on investment for H100?
Investing in new H100 units for rental is currently unprofitable. At $2.85/hour, returns barely exceed 10%. Below $1.65/hour, investments are likely to be unprofitable.
What rental models exist for H100?
There are two rental models:
How has the rise of Open-Source models affected the H100 market?
Open-Source models have increased demand for model refinement and inference, while reducing the need for H100 in training.
Why are large companies leaving public cloud platforms?
Large companies like Facebook and OpenAI build their own H100 clusters due to the need for larger clusters, accounting benefits, and the option to buy data centers.
What alternatives to H100 exist?
Other options include Nvidia L40S for inference, AMD MX300 and Intel Gaudi 3 for single-node tasks, and repurposed GPUs from cryptocurrency mining.
What are the consequences of falling H100 prices?
The price drop may cause segmentation, with large clusters costing more, and new investors potentially facing losses. It may also accelerate Open-Source model adoption.
Is it worth buying new H100 units?
In most cases, buying new H100 units is not recommended. Renting or using alternative GPUs is more cost-effective