The Generation and Serving Procedures of Typical LLMs: A Quick Explanation | HackerNoon
Transformer-based language models use autoregressive approaches for token sequence probability modeling.
Batching Techniques for LLMs | HackerNoon
Batching improves compute utilization for LLMs, but naive strategies can cause delays and waste resources. Fine-grained batching techniques offer a solution.
The Generation and Serving Procedures of Typical LLMs: A Quick Explanation | HackerNoon
Transformer-based language models use autoregressive approaches for token sequence probability modeling.
Batching Techniques for LLMs | HackerNoon
Batching improves compute utilization for LLMs, but naive strategies can cause delays and waste resources. Fine-grained batching techniques offer a solution.
Leveraging the Transformer Architecture for Music Recommendation on YouTube
Transformers can enhance music recommendations by understanding user actions within context, addressing current system limitations in predicting evolving preferences.
Deep Learning Architecture: Naive Retrieval-Augmented Generation(RAG)
Naive RAG simplifies data retrieval and generation processes through indexing, retrieving, and generating, optimizing response accuracy for user queries.
Leveraging the Transformer Architecture for Music Recommendation on YouTube
Transformers can enhance music recommendations by understanding user actions within context, addressing current system limitations in predicting evolving preferences.
Deep Learning Architecture: Naive Retrieval-Augmented Generation(RAG)
Naive RAG simplifies data retrieval and generation processes through indexing, retrieving, and generating, optimizing response accuracy for user queries.
TTT models might be the next frontier in generative AI | TechCrunch
Efficiency challenge of transformers due to increasing power demand is pushing for new architectures like test-time training models (TTT) as a potential solution.
Where does In-context Translation Happen in Large Language Models: Inference Efficiency | HackerNoon
Identifying task recognition in transformer models enables significant inference speed-ups.
TTT models might be the next frontier in generative AI | TechCrunch
Efficiency challenge of transformers due to increasing power demand is pushing for new architectures like test-time training models (TTT) as a potential solution.
Where does In-context Translation Happen in Large Language Models: Inference Efficiency | HackerNoon
Identifying task recognition in transformer models enables significant inference speed-ups.
Researchers jimmy OpenAI's and Google's closed models
Researchers discovered an attack on AI services to reveal hidden parts of transformer models through API queries.
The attack can expose the embedding projection layer of black box models, costing from a few dollars to several thousand depending on model size.
Etched scores $120M for an ASIC built for transformer models
Etched is developing an inference chip, Sohu, specialized in serving transformer models, claiming a 20x performance advantage over Nvidia's H100 by focusing on a specific type of AI model.
Etched is building an AI chip that only runs one type of model | TechCrunch
Generative AI companies are seeking alternative chip providers to challenge dominant players like Nvidia.