Abstract: Large Language Model (LLM) inference challenges memory/computing organization and dataflow optimization on traditional hardware stacks due to its various attention mechanisms and ...

Google has introduced TurboQuant, a compression algorithm that reduces large language model (LLM) memory usage by at least 6x while boosting performance, targeting one of AI's most persistent ...

The big picture: Google has developed three AI compression algorithms – TurboQuant, PolarQuant, and Quantized Johnson-Lindenstrauss – designed to significantly reduce the memory footprint of large ...

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 ...

Even if you don’t know much about the inner workings of generative AI models, you probably know they need a lot of memory. Hence, it is currently almost impossible to buy a measly stick of RAM without ...

The scaling of Large Language Models (LLMs) is increasingly constrained by memory communication overhead between High-Bandwidth Memory (HBM) and SRAM. Specifically, the Key-Value (KV) cache size ...

TurboQuant compresses AI model vectors from 32 bits down to as few as 3 bits by mapping high-dimensional data onto an efficient quantized grid. (Image: Google Research) The AI industry loves a big ...

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 ...

Long-term memory is essential for large language model (LLM) agents operating in complex environments, yet existing memory designs are either task-specific and non-transferable, or task-agnostic but ...

Abstract: We consider the issue of the memory bandwidth required for the transfer of weights of an LLM between the memory and the processor (CPU or GPU). Observing that a few exponent values dominate ...

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 more organizations run their own Large Language Models (LLMs), they are also deploying more internal services and Application Programming Interfaces (APIs) to support those models. Modern security ...