To achieve the best end-user experience for #generativeAI apps and to gain efficient use of limited and costly GPU and TPU resources, we announced several new networking capabilities that optimize traffic for #AI applications: 1️⃣ Accelerated AI training and inference with Cross-Cloud Network 2️⃣ Model as a Service Endpoint: a purpose-built solution for AI applications 3️⃣ Minimized inference latency with custom AI-aware load balancing 4️⃣ Optimized traffic distribution for AI inference applications 5️⃣ Enhance gen AI serving with Service Extensions Many of these innovations are built into Vertex AI. Now, they are available in Cloud Networking so you can use them regardless of which LLM platform you choose. Learn more → https://goo.gle/4cYZ5c8
Exciting advancements! 🚀 These new networking capabilities are game-changers for optimizing AI applications. Accelerated AI training, minimized latency, and optimized traffic distribution will undoubtedly enhance the end-user experience and make better use of GPU and TPU resources. Kudos to the team at Google for continuously pushing the boundaries of what's possible in generative AI!
Inspiring! Google always acts as an enabler for innovation. 👏🏽
Do you have a product plan for giving your customers the ability to discretely measure and report scope 1, 2, and 3 emissions related to LLM training and inference? This is important.
Very promising!
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4wThese new networking capabilities for generative AI apps are impressive, but there are concerns. Cross-Cloud Network might introduce security vulnerabilities due to data transfer across multiple clouds. Model as a Service Endpoint could limit customization for specific AI use cases. The emphasis on minimizing inference latency with custom AI-aware load balancing might not address all real-world latency issues. Traffic distribution optimization may complicate existing network infrastructure. Are there mitigations in place for these potential downsides? What measures have been taken to balance innovation with these risks?