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CMU Software Engineering Institute publishes an overview of ML training infrastructure, covering hardware considerations like GPU vs CPU and memory requirements.
The article discusses how agentic AI may shift the computing focus back to CPUs from GPUs, citing OpenAI's CFO and Ark Invest's CEO. It argues that inference for agents involves orchestration and general-purpose tasks that CPUs handle better.
Hugging Face shares community hardware statistics showing the distribution of GPUs, CPUs, and Apple Silicon among its users.
Modal explains the four key ingredients they developed to spin up serverless GPU inference replicas in seconds instead of minutes, enabling efficient GPU allocation for variable AI workloads.
The article breaks down memory bandwidth as the critical metric for local AI hardware performance, comparing current GPUs and unified memory systems from NVIDIA, Apple, AMD, Intel, and others across different performance tiers.