Categories: AI/ML Research

Attention Variants and Memory Efficiency

Overview This post is divided into eight parts; they are: • The Checkpoint Fixes the Attention Architecture • A Closer Look at Attention Memory • Multi-Head Attention • Multi-Query and Grouped-Query Attention • Sliding-Window and Mixed Attention • Multi-Head Latent Attention • What the Inference Runtime Can Change • From Cache Size to Serving Capacity The Checkpoint Fixes the Attention Architecture The checkpoint fixes the model configuration; the inference engine does not change it.

A Gentle Introduction to Multi-Head Attention and Grouped-Query Attention

This post is divided into three parts; they are: • Why Attention is Needed • The Attention Operation • Multi-Head Attention (MHA) • Grouped-Query Attention (GQA) and Multi-Query Attention (MQA) Traditional neural networks struggle with long-range dependencies in sequences.

June 20, 2025

In "AI/ML Research"

Accelerate your gen AI: Deploy Llama4 & DeepSeek on AI Hypercomputer with new recipes

The pace of innovation in open-source AI is breathtaking, with models like Meta's Llama4 and DeepSeek AI's DeepSeek. However, deploying and optimizing large, powerful models can be complex and resource-intensive. Developers and machine learning (ML) engineers need reproducible, verified recipes that articulate the steps for trying out the models on…

June 7, 2025

In "FAANG"

A Gentle Introduction to Multi-Head Latent Attention (MLA)

This post is divided into three parts; they are: • Low-Rank Approximation of Matrices • Multi-head Latent Attention (MLA) • PyTorch Implementation Multi-Head Attention (MHA) and Grouped-Query Attention (GQA) are the attention mechanisms used in almost all transformer models.

June 24, 2025

In "AI/ML Research"

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