DeepSeek recently opened the Tile Kernels repository and released DeepEP V2, detailing new GPU kernel features, a fully JIT-enabled expert parallelism redesign that boosts peak performance by up to 1.3× while cutting SM usage fourfold, and hinting at an upcoming V4 release.
AI model training relies on parallel computing, and this guide explains the four main parallelism strategies—Data Parallelism, Pipeline Parallelism, Tensor Parallelism, and Expert Parallelism—detailing their mechanisms, advantages, drawbacks, and how techniques like ZeRO and mixed 3D parallelism optimize memory and performance for massive models.
This article explains the opportunities and challenges of Mixture of Experts (MoE) models, introduces expert parallelism as a solution to scaling and deployment bottlenecks, and provides a step‑by‑step guide for deploying MoE models with Alibaba Cloud PAI‑EAS, including configuration tips and code examples.
This article explains the memory limits of a single GPU and systematically introduces data parallelism, tensor parallelism, pipeline parallelism, sequence parallelism, and expert parallelism, describing their communication costs, advantages, drawbacks, and practical implementation details for training large AI models.
The article explains the core concepts and trade‑offs of five parallelism techniques—data, tensor, context, pipeline, and expert parallelism—plus the ZeRO optimizer, showing when each method is appropriate for training ultra‑large PyTorch models and providing concrete code snippets and performance considerations.
This article analyzes Deepseek's EP‑based inference architecture for V3/R1 models, comparing it with TP, detailing how EP reduces memory and compute overhead, boosts batch size, cuts GPU memory usage, and introduces reliability, scalability, and maintainability challenges for large‑scale deployments.
