Aviz Networks Blogs

Until recently, companies sold innovative hardware. Now companies are focused more on integrated and proprietary software. AI is disrupting companies' old ways. Quickly upgradable chips and networks are no longer the most important hardware to own. Long term value comes from data, models, and workflows that utilize AI to run applications. The biggest challenge to AI businesses is: Fast-developing hardware generations. Hard-to-keep-up-with upgrades in infrastructure. Dependence on hardware for developing applications. Growing demand for deploying AI quicker at a larger scale. The answer is not decreasing hardware spending. The solution is spending to develop hardware that is more flexible and evolving. The adaptability of infrastructure allows organizations to upgrade hardware without the difficulty of rebuilding applications. Integrating upgrades in hardware allows organizations to keep their AI workflows running and evolving. Implementing operating standards is what the market is ...

The way data center networks operate is changing with the evolving nature of AI workloads. In a recent Aviz Networks podcast, Taylor Allison, Senior Product Marketing Manager at NVIDIA, spoke about changes that will happen in networking when it comes to large-scale AI training and inference. To do AI training, GPUs need to work in tandem, which requires a network that is able to communicate with low latency and stay synchronized. Once you increase the number of GPUs, even more, the existing networking technologies will start to fail. The networking solutions from NVIDIA that are optimized for AI workloads include Spectrum-X built for AI clusters over Ethernet and InfiniBand, another high-performance networking architecture for AI overclusters. The podcast also covered NVIDIA Air and the digital twin technology. Before deploying configurations and automation to the production environments, users can create virtual environments in which they can test configurations and automation. This g...

To investigate cyberattacks and suspicious activity - and to meet compliance regulations - security teams rely on evidence found within packets. However, traffic is traversing not only across their data centers, but also cloud platforms, campuses, and edges, making it challenging to capture every essential packet. Some typical problems that teams encounter are: * Overloaded packet capture systems unable to handle traffic spikes. * Irrelevant data obscuring important evidence. * Inconsistent historical records creating drag on investigations. * Higher infrastructure costs, without an increase in visibility. The Aviz Networks-Endace integrated solution makes this task simpler by guaranteeing that only optimized, high-fidelity traffic arrives at packet capture systems. Aviz Deep Network Observability (DNO) smart services efficiently aggregate, filter, dedup, and normalize traffic before directing it to Endace Always-On Packet Capture, which ingests, indexes, and permanently stores the tra...

Assembling AI infrastructure is significantly different than just installing servers and switches. Modern AI factories are highly interconnected and need to integrate networking, GPUs, storage, orchestration, and suites of applications. If configuration or integration issues appear in the deployed system, there will likely be major interruptions in the operation and costs associated with delays. Many companies struggle with: * A shortage of real-world physical lab testing. * Long validation/deployment cycles. * Discovering integration bugs at an inconvenient (read, costly!) late stage. * The hidden post-deployment rework needed to fix. That's where Aviz ONES on NVIDIA DSX Air helps deliver a better AI infrastructure build process: digital twin simulation. The idea is simple: instead of building, and then testing, why not design, simulate, and validate your entire AI networking environment beforehand? Engineers can create a model of their production-scale network, simulate traffic f...