Cloud Cost Management Strategies: Strategies for Using GPUs

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Hello! RunYourAIThis is it.

Recently, demand for proven GPUs has skyrocketed due to the generative AI craze, Cloud costs It's also growing seedlings.
In particular, since GPUs are considered for a long time to learn deep learning models, it is no interpretation to say that what kind of cloud environment is used and how to cost optimally reduces the success or failure of an AI project.

In this post Cloud cost managementLet's look at the core principles of, GPU usage StrategiesLet's take a look at how AI infrastructure can be operated

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| Why is cloud GPU cost a problem?

• The cost of GPU instances is high
  Significant to regular CPUs, GPUs with excellent computational power (especially A100, H100, A6000, etc.) are quite a few per hour. If it's an AI project that requires a long period of model training, an instantaneous billing bomb can explore.
  
  
• Data storage/network costs
  If large datasets are estimated multiple times or model checkpoints need to be backed up, the overall cost burden is estimated as storage and traffic charges estimated.
  

• The challenges of AI projects
  Deep learning model development involves a lot of trial and error and relearning. It's hard to predict exactly how much GPU time will be needed, so if you make a mistake, keep an unused GPU turned on, or you can increase costs by overrated high specs.

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| Spot Instances? The dilemma of reliability versus low cost

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In some cloud services Spot InstancesWith it, you can use a GPU at a much lower price compared to the regular price. However, Spot Resources are subject to cloud supply conditions Discontinue anytimeThere is a fatal attack of being able to do it.

• If training stops, it takes extra time to retrain the model or resume work 🥺
• Requires a long study time Generative AI (e.g. Stable Diffusion, GPT fine-tuning) and considering R&D projects are risky.

If you're not in a situation where “it's OK to stop using Spot at a reasonable price,” Reliable GPU usageYou Should Consider Other Possible Cloud Options.

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| A reliable and efficient GPU cloud usage strategy

1. Use Pay as you go (Pay as you go) only when needed

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On-demandActivate the GPU only when needed, and shut it down as soon as you're done using it Idle costsIt is a method to minimize

• Application examples: When small startups or individual students learn and verify models studied in a short time‍
• merits: Because the initial cost burden is low, and you only use as many points as you want within the charging limit Cost controlEasy to do

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✨ Runyour AI on demand

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• Point-based Pay as You Go With this method, you can use and return as many GPUs as you like
  
• Stable Diffusion, Jupyter Lab, Python Save time on environment settings by pollution templates such as
  
• Bills stop when the GPU is turned off, so there's no need to leave it on undecided

2. Save long-term projects with Reserved (bare metal servers)

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Not for a short time More than a month If you do need GPUs, Reserved products(bare metal server) might make more sense.

• High price GPU discount: Provides server-grade GPUs such as A100, H100, and A6000 at the lowest global price
  
• stability: Delivered as a bare-metal server, with less performance concerns and no risk of resource interruption to shared environments
  
• Long term contract discount: Monthly costs are reduced when used for at least 1 month
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3. Combination with Dev Cloud

If you also use a CPU-based cloud (Dev Cloud), tasks that don't require a GPU (data preprocessing, simple testing, code debugging, etc.) can be processed at a low cost.

• GPU resources Only when needed By using it, you can stop reducing waste of money.
  
• Dev Cloud Monthly recurring paymentsIt's possible, so it's great for iterative work in a stable environment.

| Extra cost saving tips💰

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Model optimization techniques

• Mixed precision trainingImprove GPU computation speed and memory usage with (FP16, BF16)‍
• Tensor parallelizationMe shardingEfficient training of large models with distributed learning technologies such as (ZERo, FSDP)
  
  

Efficient dataset operation

• Commonly used data Cloud storageStrategies to reduce redundant transfers while reducing at
• Significant intermediate results or temporary checkpoints are sometimes deleted after a certain period
  

Mixed reserved and on-demand operations

• Are long-term and long-term tasks reserved, and intermittent and test tasks are distributed on-demand
  
• Prevent costs leaks while making resource allocation flexible

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| Get started simply with Runyour AI

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If You Don't Have Spot Instances
Wouldn't it be possible to save money?

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That's not the case. Runyour AI Spot products instead,

• On-demand GPU Cloud: use only as many points as needed after charging
  
• Reserved Cloud: Use a bare-metal server dedicated to long-term projects at the lowest global price
• Dev Cloud: Processing of CPU-based tasks called 3 servicesthru Reliability and cost savings We present a structure that can catch both rabbits.

| Finalization: Reduce cost savings with stable GPUs

In the process of considering an AI project, “cloud cost management” may be considered trivial, but in reality Project budgetET ROIIt has a huge impact on

• It's not a cheap option like Spot Instances but has a high risk of interruption; On-Demand, Reserved, Dev Cloud, etc. Combined with the correctinjury Reliability and cost savingsYou can secure them at the same time.
  
  

Now don't just think of GPU clouds as worried and worried, On demand or long term bookingsTry out optimization strategies through ‍

Runyour AIWith, while smartly improving AI model training costs High performance GPU resourcesYou will be able to make full use of it.
Right now Runyour AISelect a GPU server fromLet's get started.

You don't need to worry about AI infrastructure costs We'll see you next time with some new and exciting information! 🙌