English(EN)Optimizing inference speed and costs: Lessons learned from large-scale deployments
Together AI 品牌重塑，专注于高效 AI 推理基础设施
作者PulseAugur 编辑部·[9 个来源]·
Together AI 已推出品牌重塑，强调其作为专为 AI 原生应用构建者设计的“AI 原生云”的角色。该公司正专注于优化推理效率和成本效益，这是快速扩展的 AI 产品的一个关键因素。他们正在将自适应推测解码和量化技术等先进研究整合到其平台中，以提高 Cursor 和 Decagon 等客户的性能并降低成本。
AI
影响Together AI 对优化推理基础设施和成本的关注对于 AI 原生应用的经济可行性和可扩展性至关重要。
As AI moves from research to production, the challenge for AI-native teams shifts from building models to running them — efficiently, reliably, and at scale.
AI-native companies need infrastructure built for models, not legacy workloads. Learn what defines an AI Native Cloud and why it matters for the next platform shift.
Together AI arrives at NVIDIA GTC 2026 with new launches in inference, agents, voice AI, and open models — plus technical sessions from its research and engineering leaders.
Hiring Alon Gavrielov further deepens Together AI’s commitment to building AI factories that deliver the most reliable, efficient, and scalable infrastructure for AI-native teams.
Learn how to reduce inference latency without massive cost using proven inference optimization tactics — improving throughput, GPU utilization, and cost efficiency while balancing throughput vs. latency tradeoffs.
GPUStack: cluster GPU self-hosted per inferenza AI con API OpenAI-compatibile GPUStack è uno strumento open source che trasforma macchine GPU disperse in un cluster gestito per eseguire modelli AI con API OpenAI-compatibile. Guida all'installazione, configurazione e deploy di mod…
