Large language models (LLMs) aren’t actually giant computer brains. Instead, they are effectively massive vector spaces in which the probabilities of tokens occurring in a specific order is ...

NVIDIA researchers have proposed a neural compression method for material textures that enables random-access lookups and ...

Intel and Nvidia show off how textures -- which take up a large chunk of PC games -- could be compressed to save you money ...

Intel TSNC brings neural texture compression with up to 18x reduction, faster decoding, and flexible SDK support for modern ...

NVIDIA showcases Neural Texture Compression at GTC 2026, cutting VRAM usage by up to 85% with real-time AI reconstruction.

Neural Texture Compression (NTC) could be a game-changer on par with DLSS if it can reduce the VRAM requirement for textures ...

In its "Tuscan Wheels" demo, the company showed VRAM usage dropping from roughly 6.5GB with traditional BCN-compressed ...

A new compression technique from Google Research threatens to shrink the memory footprint of large AI models so dramatically ...

The biggest memory burden for LLMs is the key-value cache, which stores conversational context as users interact with AI chatbots. The cache grows as conversations lengthen, ...

Google Research recently revealed TurboQuant, a compression algorithm that reduces the memory footprint of large language ...

Nvidia researchers have introduced a new technique that dramatically reduces how much memory large language models need to track conversation history — by as much as 20x — without modifying the model ...

Enterprise AI applications that handle large documents or long-horizon tasks face a severe memory bottleneck. As the context grows longer, so does the KV cache, the area where the model’s working ...