Cloud Computing

Large Language Models (LLMs) are powerful, but their performance can be bottlenecked by the immense NVIDIA GPU memory footprint of the Key-Value (KV) Cache. This cache, crucial for speeding up LLM inference by storing Key (K) and Value (V) matrices, directly impacts context length, concurrency, and overall system throughput. Our primary goal is to maximize […]

In our latest episode of The Agent Factory, we were thrilled to welcome Logan Kilpatrick from Google Deep Mind for a vibe coding session that showcased the tools shaping the future of AI development. Logan, who has had a front-row seat to the generative AI revolution at both OpenAI and now Google, gave us a

The world of Generative AI is evolving rapidly, and AI Agents are at the forefront of this change. An AI agent is a software system designed to act on your behalf. They show reasoning, planning, and memory and have a level of autonomy to make decisions, learn, and adapt. At its core, an AI agent

Decision makers and builders today face a constant challenge: managing rising cloud costs while delivering the performance their customers demand. As applications evolve to use scale-out microservices and handle ever-growing data volumes, organizations need maximum efficiency from their underlying infrastructure to support their growing general-purpose workloads. To meet this need, we’re excited to announce our

Today’s frontier models, including Google’s Gemini, Veo, Imagen, and Anthropic’s Claude train and serve on Tensor Processing Units (TPUs). For many organizations, the focus is shifting from training these models to powering useful, responsive interactions with them. Constantly shifting model architectures, the rise of agentic workflows, plus near-exponential growth in demand for compute, define this

Today, we are thrilled to announce C4A metal, our first bare metal instance running on Google Axion processors, available in preview soon. C4A metal is designed for specialized workloads that require direct hardware access and Arm®-native compatibility.  Now, organizations running environments such as Android development, automotive simulation, CI/CD pipelines, security workloads, and custom hypervisors can

As machine learning models continue to scale, a specialized, co-designed hardware and software stack is no longer optional, it’s critical. Ironwood, our latest generation Tensor Processing Unit (TPU), is the cutting-edge hardware behind advanced models like Gemini and Nano Banana, from massive-scale training to high-throughput, low-latency inference. This blog details the core components of Google’s

If you’re a developer, you’ve seen generative AI everywhere. It can feel like a complex world of models and advanced concepts. It can be difficult to know where to actually start. The good news is that building your first AI-powered application is more accessible than you might imagine. You don’t need to be an AI expert