Owning our technology stack end-to-end enables us to optimize every layer - something that becomes increasingly important as we deploy ever-hungrier AI workloads. When we decided to double down on our own data centers and infrastructure rather than continue… more
Owning our technology stack end-to-end enables us to optimize every layer - something that becomes increasingly important as we deploy ever-hungrier AI workloads. When we decided to double down on our own data centers and infrastructure rather than continue migrating to Azure, it really came down to timing and scale. Azure was growing at an incredible rate (and still is!) because customer demand was going through the roof, and LinkedIn was seeing its own skyrocketing growth at the same time. Building fresh for the platform we needed was the right strategic bet for LinkedIn's future. While the internet's ChatGPT moment hadn't happened yet, we already saw companies like Meta reaping the benefits of developing larger and larger AI models, and the leverage from the systems they were building to support them. We bet on building that same kind of foundation ourselves and years later, the payoff is that our engineers can design entire systems end-to-end: intelligently splitting inference workloads across GPUs and CPUs, distilling large models into smaller ones that are very good at solving a specific optimization problem, and going as deep as writing our own GPU kernels with Liger Kernel to squeeze more out of every training run. Owning all of this made our teams more disciplined and more creative at the same time. Thanks to Frederic Lardinois for a great conversation at the WeAreDevelopers Conference - video to come!88 reactions · 2 comments · – repostsOpen on LinkedIn
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