Model parallelism distributes different parts of a neural network across multiple devices. It is used when a model, its activations, or optimizer state cannot fit in the memory of one accelerator. Pipeline parallelism assigns groups of layers to different devices, while tensor parallelism divides calculations within individual layers. These approaches enable much larger models but introduce communication, synchronization, scheduling, and debugging complexity. Poor partitioning can leave hardware idle or make data transfer the bottleneck. Model parallelism is often combined with data parallelism and memory-saving techniques. The best configuration depends on architecture, sequence length, batch size, interconnect speed, numerical precision, and the balance between training throughput and inference latency.
Voice AI startup Wispr has secured another $25M in funding as its Flow dictation app surges in enterprise adoption and user retention, bringing total capital raised to $81M.
Google has released Gemini 3, its most powerful AI model yet, offering advanced multimodal reasoning, coding capabilities, and agentic planning tools across multiple platforms.
OpenAI has announced a strategic partnership with Broadcom to design and deploy 10 gigawatts of custom AI accelerators, expanding its hardware ecosystem beyond Nvidia and AMD.
Google Cloud and Figma have launched an expanded partnership to embed Google’s generative AI models – including Gemini 2.5 Flash and Imagen 4 – directly into Figma’s design tools, reducing latency and accelerating creative workflows.