Here are three papers describing different side-channel attacks against LLMs. “Remote Timing Attacks on Efficient Language Model Inference“: Abstract: Scaling up language models has significantly ...

“LLM decoding is bottlenecked for large batches and long contexts by loading the key-value (KV) cache from high-bandwidth memory, which inflates per-token latency, while the sequential nature of ...

This figure shows an overview of SPECTRA and compares its functionality with other training-free state-of-the-art approaches across a range of applications. SPECTRA comprises two main modules, namely ...

High-quality output at low latency is a critical requirement when using large language models (LLMs), especially in real-world scenarios, such as chatbots interacting with customers, or the AI code ...

Apple and NVIDIA shared details of a collaboration to improve the performance of LLMs with a new text generation technique for AI. Cupertino writes: Accelerating LLM inference is an important ML ...

In the rapidly evolving world of technology and digital communication, a new method known as speculative decoding is enhancing the way we interact with machines. This technique is making a notable ...

Have you ever been frustrated by how long it takes for AI systems to generate responses, especially when you’re relying on them for real-time tasks? As large language models (LLMs) become integral to ...

Since the groundbreaking 2017 publication of “Attention Is All You Need,” the transformer architecture has fundamentally reshaped artificial intelligence research and development. This innovation laid ...

OpenAI's new Predicted Outputs represents a significant step towards improving the user experience in LLM applications by addressing latency concerns. Latency is a significant issue for most ...