SK Hynix, Samsung and Micron shares fell as investors fear fewer memory chips may be required in the future.

A more efficient method for using memory in AI systems could increase overall memory demand, especially in the long term.

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

Google's TurboQuant algorithm compresses LLM key-value caches to 3 bits with no accuracy loss. Memory stocks fell within ...

The technique reduces the memory required to run large language models as context windows grow, a key constraint on AI ...

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

Nvidia's KV Cache Transform Coding (KVTC) compresses LLM key-value cache by 20x without model changes, cutting GPU memory ...

Forward-looking: It's no secret that generative AI demands staggering computational power and memory bandwidth, making it a costly endeavor that only the wealthiest players can afford to compete in.

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 ...

A technical paper titled “HMComp: Extending Near-Memory Capacity using Compression in Hybrid Memory” was published by researchers at Chalmers University of Technology and ZeroPoint Technologies.

Memory stocks fell Wednesday despite broader technology sector strength, with shares dropping after Google unveiled ...

Video compression has become an essential technology to meet the burgeoning demand for high‐resolution content while maintaining manageable file sizes and transmission speeds. Recent advances in ...