Speculative decoding accelerates large language model generation by allowing multiple tokens to be drafted swiftly by a lightweight model before being verified by a larger, more powerful one. This ...

Hands on When it comes to AI inferencing, the faster you can generate a response, the better – and over the past few weeks, we've seen a number of announcements from chip upstarts claiming ...

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 ...

“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 ...

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 ...

In a blog post today, Apple engineers have shared new details on a collaboration with NVIDIA to implement faster text generation performance with large language models. Apple published and open ...

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 ...