Marcin Sodkiewicz’s Post

Datadog has just launched the 2025 State of Containers and Serverless report. It's been very interesting to be a small part of putting this together. We've aggregated, analyzed and pulled together data to look at exactly how people are running their applications. From the underlying infrastructure to the managed services provided by cloud providers. My personal favourite fact, 66% of organizations using functions are also running containers. It's re-affirmed a theme I've repeated all year, the technologies are converging. It's no longer about being serverless or not serverless. It's about choosing the right technology for your workload. https://lnkd.in/eGgpR386 #serverless #containers #cloud #architecture #technology

Datadog just published a new research report! Cloud providers offer an increasing breadth of modern compute services, from serverless to containers to GPUs and beyond. For this report, we examined adoption trends across compute options, autoscaling practices, and approaches to efficiency and optimization. Our findings suggest that most organizations rely on an evolving mix of modern compute technologies, shaped by growing focus on cost optimization, shifting preference of autoscaling tools, and expanding Arm usage. Did you know Karpenter adoption has overtaken Cluster Autoscaler? Find out more in the new "State of Containers and Serverless" report. https://lnkd.in/eyPNGWKn

Everyone will get the model, but not everyone will get the renewal. Perplexity just showed how to run trillion-parameter models on standard cloud infrastructure. The bigger story is what it signals: model access is normalizing. The most important question for AI-first orgs to ask themselves isn't if they can build a smarter model, but if they can repeatably convert capability into customer value. • A workflow that eliminates effort • A result the customer can measure • Something customers will pay to keep Renewal conversations will tell the answer. https://lnkd.in/geSxh9fT

OpenAI taps AWS for serious cloud muscle—$38B and NVIDIA-powered clusters. Can this scale keep them leading the AI race? ⚡ #OpenAI #AWS #AIModels #NVIDIA #AIInfrastructure #CloudComputing https://lnkd.in/gFRMw-ZZ

AWS recently announced the general availability (GA) of general-purpose M8a instances powered by the 5th Generation AMD EPYC (codename Turin) processors. #ec2 #amazon #amd #iaas https://lnkd.in/eFed5TE5

Amazon Web Services (AWS) and OpenAI announce multi-year strategic partnership. This partnership will enable OpenAl with immediate and increasing access to AWS's infrastructure for advanced Al workloads. AWS will provide OpenAl with Amazon EC2 UltraServers, featuring hundreds of thousands of chips, and the ability to scale to tens of millions of CPUs for its advanced generative Al workloads. https://lnkd.in/edhG937i

Gartner says “I&O leaders must prioritize performance and efficiency in infrastructure decisions.” And Forrester adds “The #1 inhibitor to cloud programs is cost. Focus should be on optimization, not just cost-cutting.” Intel Xeon 6 = Performance + Price Efficiency for modern cloud strategies. How are you balancing performance vs. cost in your cloud roadmap? https://lnkd.in/g_6NkaJG #Intel #Xeon6 #AWS #CloudComputing #AI #HPC #CostOptimization #DigitalTransformation #FinOps #CIO #CTO #CloudStrategy #CloudArchitecture #IAmIntel #EnterpriseIT #DataCenter #HybridCloud #MultiCloud #CloudOps #CloudEconomics #CloudInnovation #TechLeadership #ITStrategy #Infrastructure

Perplexity is the first to develop custom Mixture-of-Experts (MoE) kernels that make trillion-parameter models available with cloud platform portability. Our team has published this work on arXiv as Perplexity's first research paper. Read more: https://lnkd.in/gStC_SzJ

Building trillion-parameter models is impressive. Making them useful is leadership. Amazon’s latest work on scaling AI infrastructure (via Neuron SDK + AWS EFA) makes it possible to train and deploy trillion-parameter models on the cloud. This isn’t about showing off compute power. It’s about reducing friction between innovation and implementation. And that’s where most companies still get stuck. “Trillion-parameter models aren’t the goal. Business value is. Use scale when it solves a real problem faster, cheaper, or better.” If you work in AI strategy, here’s what this signals: • The hardware excuse is gone. Public cloud infrastructure is now enterprise-ready for LLMs at any scale. • ROI matters more than ever. Bigger models mean higher costs. Use them where they deliver real advantage—speed, quality, or reach. • Start with fit, not size. Most use-cases don’t need trillion-parameter models. Start with lean, proven architectures. Scale later. • Governance is non-negotiable. As model size grows, so does risk. Monitoring, bias detection, and control must grow with it. • Think in stages. Don’t launch into trillion-parameter territory blind. Move in phases. Measure at each step. This kind of infrastructure shift changes what’s possible. The real question is whether your team is ready to make it valuable. Let’s stop talking about size. Let’s talk about use.

This work is a testament to the fact that the next frontier of #LLM performance and scalability lies as much in systems engineering and networking as it does in model architecture. By open sourcing these kernels, you are providing a crucial piece of infrastructure for the community, lowering the barrier to deploying trillion parameter models in a cost-effective and performant manner. This is a substantial contribution that will likely influence how future large scale inference systems are built. Excellent work from the Perplexity team. I look forward to trying the kernels and reading the full paper