The efficient frontier of LLM inference
The efficient frontier in LLM inference refers to the balance between cost and capabilities of models. A model is considered a "frontier model" if it provides the highest intelligence at a given cost or size. Inference engineering focuses on optimizing tradeoffs between latency and throughput to improve overall efficiency. Techniques include adjusting batch sizes, parallelization across GPUs, quantization, and speculative decoding.
The efficient frontier is jagged, with small changes leading to significant impacts. Improving performance through hardware and software often compounds, pushing the frontier further.
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