Dev Tools · 1h ago
CPU vs GPU: Why LLMs Need Parallel Processing
When you press Enter on a query to ChatGPT, the request travels to a server where a tokenizer converts text into numbers. The GPU then performs billions of matrix multiplications in parallel, unlike a CPU which processes tasks sequentially. This parallel architecture, originally designed for gaming, is why companies invest billions in GPUs for AI workloads.
Meridian48 take
The article offers a clear analogy but oversimplifies the engineering challenges of distributed training and inference across thousands of GPUs.
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CPU vs GPU: Why Large Language Models Need GPUs — What Really Happens After You Press Enter? →
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