Nvidia 的 CPO 技术直接解决了 AI 数据中心网络功耗和故障率两大痛点，做大规模 GPU 集群部署的团队值得关注，能显著降低运营成本。
Nvidia 发布了一段关于其光子共封装光学（CPO）交换机的视频，展示了 Lambda 技术。CPO 将光通信组件直接集成到网络芯片附近，取代了传统的可插拔模块，从而大幅降低功耗并减少故障点。在 128,000 GPU 的数据中心中，传统方案需要约 655,000 个可插拔收发器模块，而 CPO 彻底消除了这一组件类别。对于智能体工作负载，CPO 能提供弹性、高效的数据传输，避免 GPU 等待数据，提升推理效率。
Nvidia released this video of its photonics co-pac…
Nvidia released this video of its photonics co-packaged optics (CPO) switch with Lambda.
The AI race is not only about stronger GPUs, but about wasting far less power while those GPUs talk to each other.
With co-packaged optics (CPO), NVIDIA is putting the light-based communication parts much closer to the main networking chip, instead of placing them as separate plug-in modules at the edge of the switch.
From NVIDIA's official blog on this
"co-packaged optics (CPO) connects directly to the token economy. Network power is overhead: it keeps GPUs connected but doesn't generate tokens. Network failures are also overhead: they turn provisioned GPU capacity into idle capacity. CPO addresses both by reducing network power draw and removing a large class of pluggable optical components from the fabric.
A 128,000-GPU data center using traditional pluggable transceivers requires roughly 655,000 discrete transceiver modules across the switching fabric. Each one is a potential failure point. CPO removes that component class entirely.
Agentic workloads change the pressure on the network. A traditional inference request is relatively self-contained. An agentic request can involve planning, retrieval, tool use, multiple model calls, and follow-up reasoning. More data moving across the cluster. More points where network latency or failure affects the outcome.
Multi-agentic inference needs elastic and resilient data movement, so GPUs are not waiting for data, while maintaining tokens per second and fast time to first token."