Coding Tools
Together Code Sandbox Launches for AI Coding Products
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Why it matters
- →This infrastructure could enhance the development process for AI coding tools, potentially leading to more efficient and scalable solutions.
Together AI has introduced the Together Code Sandbox, designed to provide robust infrastructure for developing AI coding products at scale.
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ThunderAgent Boosts Agentic Inference Efficiency
ThunderAgent 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.
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Together AI partners with Moonshot AI for Kimi models
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.
Together AI Blog
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Together AI Enhances Model Inference Configuration
Together AI has introduced a sophisticated architecture for model inference that integrates endpoints, deployments, and configurations with capacity-aware traffic splitting. This system allows for seamless rollouts, A/B testing, and zero-downtime updates, making it easier for developers to manage and optimize AI models. By using immutable configurations and a weight-based traffic split, the platform ensures efficient resource allocation and scaling. This development simplifies the deployment process and enhances the reliability of AI applications by ensuring consistent performance and easy rollback options.
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