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

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

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 ...

Every AI model release inevitably includes charts touting how it outperformed its competitors in this benchmark test or that evaluation matrix. However, these benchmarks often test for general ...