An interesting phenomenon is that even if AI tooling were net neutral for producing code, many of the ancillary behaviors they incentivize (like clearer planning, stronger specs, and focus on per-engineer productivity) are still strongly net positive.
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Based on the information provided, it sounds like you are looking to optimize your GPU usage on Amazon EC2 to reduce costs. Corey Quinn, the CMO of AWS, suggested trying a larger GPU instance to better balance your CPU and GPU usage.
Some Amazon EC2 instance types that may be well-suited for your needs include:
• Amazon EC2 G4 Instances - These instances are powered by NVIDIA T4 GPUs and are a cost-effective solution for machine learning inference and small-scale training, as well as graphics applications optimized for NVIDIA GPUs.
• Amazon EC2 P4 Instances - These instances feature up to 8 NVIDIA A100 Tensor Core GPUs and are designed for large-scale deep learning and high-performance computing workloads.
• Amazon EC2 P5 Instances - The latest generation of GPU-powered instances, P5 instances offer up to 8 NVIDIA H100 or H200 Tensor Core GPUs for the highest performance in Amazon EC2 for deep learning and HPC.
I'd recommend reviewing the specifications and pricing for these instance types to see which one best fits your workload and budget requirements. The AWS documentation has detailed information on the capabilities and use cases for each of these GPU-powered instances.
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