Single GPU

Single-GPU computing remains a crucial area of research, focusing on optimizing the performance and energy efficiency of various machine learning tasks, including large language model (LLM) inference, image generation, and other computationally intensive algorithms. Current research emphasizes efficient memory management, novel attention mechanisms (like linear attention), and optimized kernel designs to maximize throughput and minimize latency, often targeting specific model architectures like Transformers and diffusion models. These advancements are significant because they enable cost-effective deployment of powerful AI models on readily available hardware, broadening access to advanced computational capabilities and accelerating progress across diverse scientific and industrial applications.