CPU vs GPU vs TPU

📚 Main topics

• Different chip architecturesCPUs are flexible processors with a small number of powerful cores, GPUs handle large amounts of parallel computation, and TPUs are specialized for machine-learning workloads. 0:01
• Workloads suited to CPUsCPUs are effective at tasks with varied steps, branching, and decision-making, such as web servers, databases, operating systems, and application logic. 0:31
• Parallel computation and matrix multiplicationGPUs are well suited to repeating the same calculations across large datasets. Matrix multiplication is a common example, appearing throughout neural-network processing. 1:01

• Tensors and TPUsTensors are multidimensional arrays of numbers, such as batches of images. TPUs are designed for tensor-heavy machine-learning tasks, including neural-network training and inference. 3:07

  ✨ Key takeaways

• Match hardware to the workloadCPUs handle varied control-flow tasks, GPUs excel at many parallel workloads, and TPUs can be highly efficient for machine-learning operations that fit their design. 4:10

• Specialization involves trade-offsMore specialized hardware can improve efficiency for certain workloads, but it is less flexible than a general-purpose CPU. 4:10

  🧠 Lessons learned

• Why GPUs help with AINeural networks perform repeated mathematical operations across inputs and layers, making their workloads a good fit for GPU parallelism. 2:36

• Why TPUs can shineLarge-scale tensor operations, including those used in transformer training and large language model inference, align with TPUs’ specialization. 3:38

  🏁 Conclusion / next steps

• Use different chips for different tasksModern systems can combine processors, using CPUs for control flow and orchestration and accelerators for suitable compute workloads. The key is matching the workload to the right architecture. 4:42