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GPU acceleration is limited by the fraction of your workload that's actually parallel. Amdahl's Law states: if 90% of a program can be parallelized, the maximum speedup from infinite parallel processors is 10× — not infinite. The 10% sequential bottleneck always limits total speedup. In GPU-accelerated ML training
Benchmarks showing "GPU is 100× faster than CPU for AI" are technically accurate but contextually misleading. CPUs remain essential for everything that sits around the AI workload: loading data from disk and preprocessing it, orchestrating which jobs run in what order, handling HTTP requests that trigger model inference, aggregating and returning results.
Here's the thing most tutorials miss: Server-Sent Events (SSE) is a third option between long polling and WebSockets. SSE is HTTP-based (works through standard HTTP/2 multiplexing), server-push only (client-to-server still uses regular HTTP requests), and has automatic reconnection built in.