Edge AI addresses high-performance, low-latency requirements by embedding intelligence directly into industrial devices.

Evolving challenges and strategies in AI/ML model deployment and hardware optimization have a big impact on NPU architectures ...

Local AI concurrency perfromace testing at scale across Mac Studio M3 Ultra, NVIDIA DGX Spark, and other AI hardware that handles load ...

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 ...

The reason why large language models are called ‘large’ is not because of how smart they are, but as a factor of their sheer size in bytes. At billions of parameters at four bytes each, they pose a ...

I am encountering an issue while attempting to quantize the Qwen2.5-Coder-14B model using the auto-gptq library. The quantization process fails with a torch.linalg.cholesky error, indicating that the ...

Abstract: The increasing adoption of machine learning at the edge (ML-at-the-edge) and federated learning (FL) presents a dual challenge: ensuring data privacy as well as addressing resource ...

Mathematical reasoning stands at the backbone of artificial intelligence and is highly important in arithmetic, geometric, and competition-level problems. Recently, LLMs have emerged as very useful ...