AI models code simple games, but struggle to play them ...

Google researchers have proposed TurboQuant, a method for compressing the key-value caches that large language models rely on ...

MLPerf results show how new GPUs and system-level design are enabling faster, scalable inference for large language models ...

What if the tools we trust to measure progress are actually holding us back? In the rapidly evolving world of large language models (LLMs), AI benchmarks and leaderboards have become the gold standard ...

Google's TurboQuant reduces the KV cache of large language models to 3 bits. Accuracy is said to remain, speed to multiply.

As large language models (LLMs) continue to improve at coding, the benchmarks used to evaluate their performance are steadily becoming less useful. That's because though many LLMs have similar high ...

As large language models (LLMs) continue their rapid evolution and domination of the generative AI landscape, a quieter evolution is unfolding at the edge of two emerging domains: quantum computing ...

This illustrates a widespread problem affecting large language models (LLMs): even when an English-language version passes a ...