Running a 70-billion-parameter large language model for 512 concurrent users can consume 512 GB of cache memory alone, nearly four times the memory needed for the model weights themselves. Google on ...

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 ...

As AI workloads extend across nearly every technology sector, systems must move more data, use memory more efficiently, and respond more predictably than traditional design methodologies allow. These ...

AMD recently published a new patent that reveals that the company is working on making its 3D V-cache tech even better. Back in early 2021, we started hearing the first whispers and murmurs of a new ...

DRAM access latency is typically 50–100 ns, which at 3 GHz corresponds to 150–300 cycles. Latency arises from signal propagation, memory controller scheduling, row activation, and bus turnaround. Each ...

NVIDIA introduces NVFP4 KV cache, optimizing inference by reducing memory footprint and compute cost, enhancing performance on Blackwell GPUs with minimal accuracy loss. In a significant development ...

ABSTRACT: The golden age of digital chips seems to be coming to an end. For decades, we have relied on making transistors smaller and increasing clock speeds to improve performance. However, when chip ...

Hi, I'm trying to use ExtMemQuantileDMatrix for training huge dateset on gpus. For example, training 1Tb raw fp32 dataset on 4/8xRTX 4090 (24G) + 2/4Tb memory (which is sufficent for the same dataset ...