Memory bandwidth is the primary bottleneck for autoregressive LLM decoding. Learn how draft models generate token candidates verified in parallel by the target model.
1. The Memory Bandwidth Bottleneck in Autoregressive LLMs
During standard autoregressive token generation, generating a single token requires loading all 70B parameter weights from GPU High Bandwidth Memory (HBM3) to SRAM. Because arithmetic intensity is low, the GPU tensor cores sit idle waiting for memory transfer.
Written by Dr. Sarah Chen
Focusing on speculative decoding, quantization, and real-time LLM inference pipelines.
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