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Transformers2 articles

Transformers

Articles

  • Tensor Parallelism in Large Language Models: How Megatron-LM Partitions Multi-Layer Perceptrons and Attention Heads

    Tensor Parallelism in Large Language Models: How Megatron-LM Partitions Multi-Layer Perceptrons and Attention Heads Training and serving modern large language models requires navigating severe hardware memory and compute constraints. While standard Distributed Data Parallelism (DDP) replicates the entire model across multiple accelerators, modern frontier architectures containing tens or hundreds of billions of parameters exceed the physical memory capacity of any single GPU. Even with 80 GB o

    1 min

  • Grouped-Query Attention: How Head Sharing Slashed KV Cache Memory and Scaled LLM Serving

    Serving large language models at scale presents a fundamental hardware bottleneck: autoregressive decoding is bound by memory bandwidth rather than compute. While the initial prompt processing phase (prefill) operates as compute-bound matrix multiplications, token-by-token generation requires loading billions of cached attention states from GPU High-Bandwidth Memory (HBM) to on-chip SRAM for every single generated token. Multi-Head Attention (MHA), introduced in the foundational Vaswani et al.

    1 min