The True Cost of Compute | Summary and Q&A

TL;DR

Training large language models costs millions of dollars, with compute resources becoming a determining factor for the success of AI companies.

Key Insights

• 🧑‍🏭 Training large language models can cost millions of dollars, with compute resources being a determining factor for the success of AI companies.
• 💻 Many companies spend a significant portion of their capital on compute resources, often more than 80%.
• 💨 The cost of training models may decrease in the future as chips get faster and access to training material becomes limited.
• 🙊 Inference is much cheaper than training, but the cost depends on the model's size and the demand for peak capacity.
• ☠️ Factors such as batch size, learning rate, and training duration can contribute to the final price tag of training a model.
• 🖐️ The size of the model and the amount of training data also play a significant role in the cost of training.
• 💻 Compute demand is unlikely to subside in the future, as AI continues to advance.

Transcript

there's very few computational problems that complex that mankind has you know undertaken so if napkin math 175 billion parameters 350 billion floating Point operations three times ten to the 23 and that's a completely crazy number got it got it expectation at the moment is that the cost for training these models may actually sort of top out or eve... Read More

Questions & Answers

Q: How much does it cost to train a large language model?

Training one of these models can cost millions of dollars, with some companies spending more than 80% of their capital on compute resources.

Q: What factors contribute to the final price tag of training a model?

Factors such as batch size, learning rate, and training duration can all contribute to the cost of training a model. The size of the model and the amount of training data also play a significant role.

Q: Is compute demand expected to decrease in the future?

Compute demand is unlikely to subside, especially as AI continues to advance. However, the cost of training models may decrease as chips get faster and training material becomes limited.

Q: How does inference cost compare to training cost?

Inference is much cheaper than training, with the cost depending on the model's size and the demand for peak capacity. Inference can often be done on a single card, reducing costs.

Summary

In this video, Guido Apenzeller discusses the relationship between compute capital and AI technology in terms of cost. Training large language models can be extremely expensive, with some companies spending more than 80% of their total capital on compute resources. The cost to train a model depends on factors such as the number of parameters and floating-point operations. While training costs are high, the cost for inference is much cheaper. The relationship between compute capital and technology is a complex one, and as the AI boom continues, the cost of training models may drop while the need for compute resources remains high.

Questions & Answers

Q: How does the cost of compute resources impact AI companies?

Access to compute resources has become a determining factor for the success of AI companies. Many companies are spending a significant portion of their capital on compute resources, often more than 80%. This cost is especially impactful for startups that want to train their own models, as they have to allocate a large chunk of their funding to compute capacity. However, as companies evolve and move towards more complete product offerings, the percentage of capital spent on compute resources is expected to decrease over time.

Q: How is the cost of training AI models calculated?

The cost of training a model depends on various factors, but for the majority of models, especially Transformer models, the training time can be approximated as about six times the number of parameters. To determine the compute capacity needed, one can estimate the number of floating-point operations required, which is twice the number of parameters for inference. For example, the model GPT-3 has 175 billion parameters, requiring approximately 350 billion floating-point operations for inference. These calculations provide a rough estimate of the compute capacity needed and the associated cost.

Q: Can the cost of training AI models be reduced through optimization?

There are various ways to optimize the compute cost of training AI models. One approach is to use reduced precision, which can save on compute resources. However, achieving 100% utilization on AI accelerator cards can be challenging, and typically, utilization is below 10% with naive implementation. By implementing optimizations and maximizing utilization, it is possible to reduce the compute cost. However, it is important to test these assumptions and ensure they hold before making any final decisions.

Q: How much does it cost to train models like GPT-3?

Training models like GPT-3, which has 175 billion parameters, can be extremely expensive. The rough estimate for the number of floating-point operations required for training is around 3 times 10 to the power of 23. If we consider using a commonly used card like the A100, which can perform a certain number of floating-point operations per second, the cost can be estimated. Naively analyzing this, it can cost approximately half a million dollars, but this is a simplified calculation and doesn't consider optimization, the need for multiple runs, and other factors. In practice, training large language models can cost tens of millions of dollars.

Q: Is inference significantly cheaper than training AI models?

Yes, inference is much cheaper than training AI models. For modern text models, the training set can be about a trillion tokens, and each token generated during inference is treated as one inference. The inference cost is a fraction of a cent per token, typically in the range of a tenth or hundredth of a cent. Inference costs can be further reduced by using cheaper cards, especially for models like image generation, which can run on consumer graphics cards. However, provisioning for peak capacity can still increase the cost of inference.

Q: Does having more compute resources lead to better models?

Generally, more compute resources can contribute to better models, but it is not always a direct correlation. The size of the model, represented by the number of parameters, needs to be balanced with the amount of training data available. Having a super large model with limited data or a large amount of data with a small model may not produce the desired results. The optimal size of the model depends on the available training data and the problem being solved. As large language models already leverage a substantial amount of human knowledge, going beyond a certain scale may not provide significant improvements. Therefore, the cost for training models may eventually top out or even decrease as models reach a reasonable size relative to the available data.

Q: How does the cost of compute impact new entrants in the AI space?

Training large language models is an expensive endeavor, and the high cost has limited the ability of smaller players to enter the market. However, the cost for training these models overall seems to be coming down, and the expectation is that the cost may top out or decrease as compute technology improves. While heavily capitalized incumbents may have an advantage in the short term, the cost of training large language models is within reach for well-funded startups. This is likely to drive more innovation in the AI space and enable new entrants to compete.

Q: How does the cost of compute resources impact the AI industry as a whole?

The cost of compute resources has a significant impact on the AI industry. It determines the feasibility of training large language models and the ability of startups and companies to compete. While the cost of training models may decrease over time, the need for compute resources will remain high as AI continues to advance. Compute capital is a crucial factor in the competition for AI hardware, and companies that can invest in compute resources have an advantage. However, as the AI boom is still in its infancy, the landscape may evolve, and new innovations can change the dynamics of compute costs in the future.

Q: Can the cost of compute resources be reduced through advancements in AI technology?

Advancements in AI technology can potentially reduce the cost of compute resources. As AI models become more optimized and efficient, they may require fewer compute resources for training. Additionally, advancements in hardware technology can also lead to faster and more efficient compute capabilities, which can reduce costs. However, the relationship between compute capital and AI technology is complex, and the cost of compute resources may depend on various factors, such as the availability of training data and the size of the models.

Q: Can the cost of compute resources be offset by piecing together less performance chips?

When it comes to model training, the inefficiencies of piecing together less performance chips cannot be easily offset. Distributed training with multiple chips requires sophisticated software to manage data distribution, and the overhead of this distribution can outweigh any cost savings from using cheaper chips. However, for inference, it is possible to use cheaper cards, such as consumer graphics cards, for certain models like image generation. In this case, the cost of compute resources can be reduced significantly. Nonetheless, for model training, investing in high-performance chips is often necessary to achieve efficient and effective training.

Takeaways

The cost of compute resources is a critical factor in the success of AI companies. Training large language models can cost millions or even tens of millions of dollars. While training costs are high, inference costs are much lower. The relationship between compute capital and AI technology is complex, and the cost of compute resources may fluctuate as technology advances and optimizations are made. However, the need for compute resources will remain high as AI continues to evolve. The cost of compute resources can impact new entrants in the industry, but as the AI boom progresses, the cost of training models may decrease, making it more accessible to startups. Overall, the cost of compute resources plays a significant role in the AI industry and will continue to shape its development.

Summary & Key Takeaways

• Training large language models costs millions of dollars and is a determining factor for AI companies' success.

• Many companies spend more than 80% of their total capital on compute resources.

• The cost of training models may decrease in the future as chips get faster and training material becomes limited.