Google unveils TurboQuant to reduce AI model memory usage

Google introduces TurboQuant, a compression method that reduces memory usage and increases speed ...

The Five Trends Driving Memory To The Forefront Of AI Scaling

Memory is no longer just supporting infrastructure; it's now become a primary determinant of system performance, cost and ...

Ollama is supercharged by MLX's unified memory use on Apple Silicon

Machine learning researchers using Ollama will enjoy a speed boost to LLM processing, as the open-source tool now uses MLX on ...

Google’s TurboQuant AI-compression algorithm can reduce LLM memory usage by 6x

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

Nvidia says it can shrink LLM memory 20x without changing model weights

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

Fastest AI Vision Model for Your Laptop : Liquid AI LFM 2.5

Liquid AI’s LFM 2.5 runs a vision-language model locally in your browser via WebGPU and ONNX Runtime, working offline once ...
Semiconductor Engineering

Developing ReRAM As Next Generation On-Chip Memory For Machine Learning, Image Processing And Other Advanced CPU Applications

In modern CPU device operation, 80% to 90% of energy consumption and timing delays are caused by the movement of data between the CPU and off-chip memory. To alleviate this performance concern, ...
Semiconductor Engineering

Will In-Memory Processing Work?

The cost associated with moving data in and out of memory is becoming prohibitive, both in terms of performance and power, and it is being made worse by the data locality in algorithms, which limits ...