Category: FAANG

There’s a strange loop taking over social media right now. Scroll through TikTok, YouTube Live, or Instagram, and you’ll see a parade of “digital marketing experts” promoting their latest PDF guide, online course, or coaching program. What’s it about? Digital marketing. But not the kind that helps actual businesses improve performance, it’s a course on …

Read more “Digital Marketing Courses to Sell Digital Marketing Courses”

Long chain-of-thought (CoT) significantly enhances large language models’ (LLM) reasoning capabilities. However, the extensive reasoning traces lead to inefficiencies and an increased time-to-first-token (TTFT). We propose a novel training paradigm that uses reinforcement learning (RL) to guide reasoning LLMs to interleave thinking and answering for multi-hop questions. We observe that models inherently possess the ability …

Read more “Interleaved Reasoning for Large Language Models via Reinforcement Learning”

Mixture-of-Experts (MoE) models are crucial for scaling model capacity while controlling inference costs. While integrating MoE into multimodal models like CLIP improves performance, training these models is notoriously challenging and expensive. We propose CLIP-Upcycling (CLIP-UP), an efficient alternative training strategy that converts a pre-trained dense CLIP model into a sparse MoE architecture. Through extensive experimentation …

Read more “CLIP-UP: A Simple and Efficient Mixture-of-Experts CLIP Training Recipe with Sparse Upcycling”

At Google Cloud, we’re committed to providing the most open and flexible AI ecosystem for you to build solutions best suited to your needs. Today, we’re excited to announce our expanded AI offerings with Mistral AI on Google Cloud:  Le Chat Enterprise on Google Cloud Marketplace: An AI assistant that offers enterprise search, agent builders, …

Read more “Mistral AI’s Le Chat Enterprise and Mistral OCR 25.05 model are available on Google Cloud”

With the rapid expansion in the scale of large language models (LLMs), enabling efficient distributed inference across multiple computing units has become increasingly critical. However, communication overheads from popular distributed inference techniques such as Tensor Parallelism pose a significant challenge to achieve scalability and low latency. Therefore, we introduce a novel optimization technique, Sync-Point Drop …

Read more “SPD: Sync-Point Drop for Efficient Tensor Parallelism of Large Language Models”