Months of hands-on testing with locally run large language models (LLMs) show that raw parameter count is less important than architecture, context window, and memory bandwidth. Advances in ...
turboquant-py implements the TurboQuant and QJL vector quantization algorithms from Google Research (ICLR 2026 / AISTATS 2026). It compresses high-dimensional floating-point vectors to 1-4 bits per ...
As Large Language Models (LLMs) expand their context windows to process massive documents and intricate conversations, they encounter a brutal hardware reality known as the "Key-Value (KV) cache ...
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 ...
Abstract: We investigate information-theoretic limits and design of communication under receiver quantization. Unlike most existing studies that focus on low-resolution quantization, this work is more ...
Abstract: Quantization is a crucial technique for deploying Large Language Models (LLMs) in resource-constrained environments. However, minimizing performance degradation due to outliers in activation ...
Experts At The Table: AI/ML is driving a steep ramp in neural processing unit (NPU) design activity for everything from data centers to edge devices such as PCs and smartphones. Semiconductor ...
It turns out the rapid growth of AI has a massive downside: namely, spiraling power consumption, strained infrastructure and runaway environmental damage. It’s clear the status quo won’t cut it ...
Hardware-accelerated YOLO11 object detection on Xilinx Zynq-7020 FPGA (PYNQ-Z2 board) using Keras 3, HGQ2, and HLS4ML. yolo11_zynq_deployment/ ├── config.yaml # Configuration file ├── requirements.txt ...
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