
A new initiative aims to deploy 100,000 GPUs across Europe to enhance AI capabilities and infrastructure. This move is expected to support various AI applications and research efforts in the region.
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© Together AI BlogThunderAgent introduces a novel approach to agentic inference, significantly improving throughput and reducing latency in synthetic data generation. By treating each agent workflow as a program rather than isolated requests, it mitigates KV cache thrashing and balances load across nodes. This results in up to 2.5× higher throughput on single nodes and near-linear scaling on multi-node clusters. ThunderAgent's compatibility with existing inference optimizations makes it a practical choice for enhancing large-scale agentic workloads.
© Together AI Blog
© TechCrunch AIMicrosoft is positioning itself as a formidable competitor to AI giants OpenAI and Anthropic by promoting its own AI models and infrastructure. CEO Satya Nadella emphasizes the importance of enterprises maintaining control over their AI systems, advocating for a diverse model approach to avoid dependency on any single provider. This strategy is underscored by Microsoft's development of the MAI family of models and the Maya AI chips, which promise cost-effective and efficient performance. By offering a broad catalog of models, Microsoft aims to provide enterprises with flexible and secure AI solutions, challenging the dominance of established AI labs.
Together AI's partnership with Moonshot AI marks a significant step in making cutting-edge AI models more accessible to developers. By hosting Moonshot's Kimi models, including the 2.8 trillion parameter Kimi K3, Together AI offers developers immediate access to powerful open-weight models. This collaboration allows for seamless integration and post-training capabilities, enabling developers to fine-tune models for specific applications. The partnership promises to deliver high-performance AI solutions with the flexibility and scalability that open models provide, challenging proprietary systems in the market.
The music industry is taking a significant step towards AI governance with a coalition of major and independent labels proposing principles for AI-generated music chart eligibility. This initiative, alongside a new AI labeling program, aims to establish a framework for transparency and accountability in AI music production. By standardizing AI metadata and disclosure, the industry hopes to improve royalty administration and reduce fraud. While legal challenges remain, this collaborative effort marks a pivotal move towards managing AI's impact on music.