When working with deep learning models or large-scale machine learning tasks, one of the first decisions you’ll face is whether to use CPUs or GPUs. Both have unique strengths and limitations, and the best choice depends on your budget, performance requirements, and time constraints.
PUs: The Budget-Friendly Option
Central Processing Units (CPUs) are the traditional workhorses of computing. They are versatile and capable of handling a wide variety of tasks, but they’re not optimized for the intense parallel computations required by deep learning.
Key Characteristics of CPUs:
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Less expensive: A CPU-based setup is often more cost-effective, especially for smaller workloads.
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Lower concurrency: CPUs are designed for general-purpose computing rather than large-scale parallel processing.
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Slower for deep learning: Training models on CPUs can take much longer compared to GPUs.
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Good for low-priority tasks: If time isn’t critical, CPUs are a great way to save costs.
When to choose CPUs:
If you’re experimenting, building prototypes, or have flexible deadlines, CPUs may be the ideal choice.
GPUs: The Powerhouses of Parallel Processing
Graphics Processing Units (GPUs) are designed for handling many operations at once—perfect for deep learning, where massive parallel computation is key.
Key Characteristics of GPUs:
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More expensive: High performance comes at a higher cost.
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High concurrency: GPUs are built for parallel processing, significantly speeding up training times.
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Optimized for deep learning: Ideal for both training and inference of complex models.
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Different power levels: The GPU requirements for training and inference can differ—training generally requires more power.
When to choose GPUs:
If you value speed and efficiency, especially for model training and large-scale inference, GPUs are the right investment.
Cluster Size: Balancing Cost and Responsiveness
When running jobs on cloud infrastructure, cluster size plays a major role in performance and cost.
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A small or zero-node cluster will save money but may result in slower response times since it needs to “spin up” new nodes when a job starts.
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A larger starting cluster ensures faster responsiveness but increases costs.
Rule of thumb:
If you have more time but less money, use a smaller cluster.
If you have less time but more money, go for a larger one.
VM Size: Scaling Resources to Fit Your Needs
Virtual Machines (VMs) vary widely in their specifications. Performance and cost depend on features like:
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RAM
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Disk size
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Number of cores
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Clock speed
Choosing a larger VM with more memory, storage, and cores will improve performance—but also increase costs. The right balance depends on your workload type (training vs. inference) and budget.
Dedicated vs. Low-Priority Instances
Finally, you’ll need to decide whether to use dedicated or low-priority (interruptible) instances.
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Low-priority instances:
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Cheaper, but can be interrupted if the cloud provider reallocates resources.
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Best for non-critical or easily restartable jobs.
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Dedicated instances:
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Guaranteed uptime; your job won’t be terminated unexpectedly.
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More expensive but reliable for long or critical workloads.
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Selecting the right combination of CPU vs GPU, cluster size, VM size, and instance type is all about trade-offs between time and money.