Megaport

Explore 9 practical networking use cases to help you build secure, scalable hybrid and multicloud connectivity.

We spend a lot of time talking about which GPUs are best for AI. But a GPU can only process a request after that request reaches it. #ad If the compute is far from your users or data, the application can still feel slow. This is why where the servers live and how they are connected matters just as much as how much compute you have. So if your company is building an AI application, don’t just ask: “How many GPUs do we need?” Also ask, “Where will that compute run, and how will we connect it?” Megaport is working on this with its AI Inference Cloud, which gives businesses access to CPUs and GPUs across different regions and connects them through Megaport’s global network. In this video, I explain what AI inference actually is and why the location of your infrastructure matters. Learn more: https://lnkd.in/geRnGvfB #MegaportPartner

𝗪𝗵𝗲𝗻 𝘀𝗵𝗼𝘂𝗹𝗱 𝗔𝗜 𝘄𝗼𝗿𝗸𝗹𝗼𝗮𝗱𝘀 𝗿𝘂𝗻 𝗼𝗻 𝘃𝗶𝗿𝘁𝘂𝗮𝗹 𝗺𝗮𝗰𝗵𝗶𝗻𝗲𝘀 𝘃𝘀. 𝗯𝗮𝗿𝗲-𝗺𝗲𝘁𝗮𝗹 𝗚𝗣𝗨𝘀? The answer depends on how the workload uses the hardware. 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗠𝗮𝗰𝗵𝗶𝗻𝗲𝘀 Modern GPU virtualization can get close to native performance. → Recent MLPerf inference tests reached 95% of bare-metal performance → GPU capacity can be shared across isolated workloads instead of dedicating an entire server → Capacity can be provisioned, resized, and reassigned as workloads change For smaller inference jobs, development environments, and variable workloads, this can mean much better utilization of expensive GPU capacity. 𝗕𝗮𝗿𝗲-𝗠𝗲𝘁𝗮𝗹 𝗚𝗣𝗨𝘀 Now consider Qwen3.5-397B, a multimodal model with 403 billion parameters in total. The weights alone require roughly 806 GB of GPU memory. An NVIDIA RTX PRO 6000 has 96 GB. At this scale, models need to be sharded across multiple GPUs. And once a workload spans GPUs, memory bandwidth and communication between GPUs become part of the performance equation. That is where dedicated multi-GPU infrastructure starts to matter. The choice isn’t simply VM vs. bare metal. It’s about the workload: → How much GPU memory does it need? → How many GPUs does it span? → How much communication happens between GPUs? → How quickly does capacity need to change? Latitude.sh, now part of Megaport, brings compute, storage, and networking together, with virtual machines or dedicated bare-metal GPUs depending on the workload. 🔗 Learn more: https://lnkd.in/g3JPE8QG 📌 Save this for your next AI infrastructure project. #MegaportPartner #GPU #VM #AIExplained

Let’s say your company was hacked today. Could you answer these 3 questions? ➀ Do you have a backup of your data? ➁ Do you have a backup of your environment? ➂ How fast would you be able to restore your data and environment? Hopefully you could, and answered yes to at least the first two because cyber resiliency is more than just preventing attacks. It’s also about ↳ layering in defense in depth to minimize the impact of attacks ↳ ensuring you can recover in minimal time ↳ having protected, versioned backup copies ↳ a clean environment where you can validate everything before bringing systems back online You don’t want to be in a position where you’re actually in an incident and overlooked having backups. And you can recover your backups fast with Megaport Storage connected directly to Megaport’s private network with ↳ direct access to storage at up to 100Gbps ↳ standard 100 Gbps up to 400 Gbps network port options ↳ no egress fees ↳ object lock for protected backup copies how quickly you can restore your data determines how long a cyber incident can bring your company down #megaportpartner #cyberresilience #storage #backuprecovery

Wrote a rap about data centers 🔥 Megaport hired me to make a music video about compute, networks, and ports… …and I got to walk inside a real data center for the first time. Insane experience. - As a rapper you gotta be an all in one hub. Music, lyrics, visuals, brand, etc. Megaport is that hub for infrastructure. Compute, network, storage, one connection. Real recognize real. - Backstory: I met Alexis Bertholf back in 2023 at a creator house. Been kicking around a collab ever since. Thanks to Megaport we finally made it happen. Alexis is changing how infrastructure tech gets marketed and Megaport is leading that same charge. Fun fact: I filmed the vid in my RM Williams Chelsea boots. IYKYK. Shout out to Australia 🇦🇺 - Tag your favorite network engineer in the comments! #MegaportPartner

I’ve been down an AI infrastructure rabbit hole lately to answer how companies looking to run large AI models handle technical and operational scaling. As it turns out, the solutions to these questions depend on three systems working together: 1. “Compute” aka the CPUs and GPUs running the model 2. “Storage” aka the data, files, and context behind the response 3. “Network” aka the layer that moves everything between users, clouds, data center, compute, and storage This all stays invisible when you’re just talking to AI, but when companies expand their AI training and deployment blueprint, they start talking about: ↳ Cloud costs getting out of hand ↳ Finding AI infrastructure / GPU capacity ↳ Needing more bandwidth These sound like separate issues, but in actuality point to the same issue: modern infrastructure is usually bought in pieces, managed in silos, and then expected to work together perfectly to support high demand AI workflows. and this is what Megaport solves for: bringing compute, network, and storage together in one automated infrastructure platform. Instead of treating each piece as a separate project, teams can deploy what they need, connect it across locations, and adapt as their AI needs change. If you want to learn more about this with me, check out https://lnkd.in/gS_Fm5pN #megaportpartner #aiinfrastructure #informationtechnology #cloud