Inference Efficiency, Not GPU Count, Drives Large Model Performance

The AI industry has focused heavily on larger models and more GPUs, but the article argues that how efficiently those resources are used matters just as much. Serving a large language model involves scheduling requests, managing memory, coordinating devices, caching, and keeping costly hardware busy. Techniques such as disaggregated inference, mixture-of-experts parallelism, dynamic load balancing, and speculative decoding can substantially lower serving costs, and DeepSeek's approach is cited as an example. Inference splits into a compute-heavy prefill phase and a memory-bandwidth-limited decode phase, which can be separated onto different hardware pools for independent scaling and better utilization.