Less than a week to the Alibaba Cloud x TiDB AI Innovation Night - and seats are filling up fast! From agentic AI and AI-ready data infrastructure to real-world deployment strategies and measurable ROI, hear how enterprises are turning AI ambition into business impact. Connect with industry leaders and peers over an evening of conversations, networking, dinner, drinks, and a few surprise elements along the way. Register now: Lumen #AlibabaCloudSG# #AlibabaCloudPartner# #AlibabaCloud# #AI# #DigitalTransformation# #LLM# #Qwen# #Wan# #ContentCreation# #DigitalUpskilling# #CloudComputing# #AInnovation# #TiDB# #LingYang# #AgenticAI# #GenAI#

Kimi K2.6 is now available on Bitdeer AI Cloud. Built for advanced coding, long-horizon execution, and more capable agent workflows, Kimi K2.6 gives developers and teams another powerful model option to build, test, and scale on our platform. Key highlights: 🔹Strong long-horizon coding capabilities 🔹Advanced technical and frontend code generation 🔹Better handling of complex, multi-step tasks 🔹Well suited for research, analysis, and structured generation Run Kimi K2.6 on our Model Studio today 👉: #KimiK26# #ModelStudio# #AIModels# #agenticai#

We’re seeing strong adoption of #OpenClaw#, alongside growing concerns around security and reliability. Building an AI agent is easy🤖. Running it in production is not. Local deployment often introduces downtime, complex setup, and risks around API key and environment security. For enterprise use, AI agents must be always on, secure, and scalable. This is why more teams are moving to the cloud. With Bitdeer AI Cloud, you can run AI agents with 24/7 uptime, secure environments, and on-demand scalability. As AI agents move into real business workflows, the focus is shifting from building to running them properly. Read our latest blog to learn more: #neocloud# #agenticAI#

From concept to deployment, our latest guide shows how to run secure AI agents with NVIDIA #NemoClaw# on Bitdeer AI Cloud🤖. Learn how to launch a fully sandboxed #agent# in minutes, connect to powerful models like NVIDIA-Nemotron-3-Super-120B-A12B, Moonshot AI’s Kimi-K2.5 through Bitdeer AI’s high performance inference endpoints, and apply network policy controls with greater operational visibility. Explore the guide and start deploying your own secure AI agents on Bitdeer AI Cloud today: #AgenticAI# #AIInfrastructure# #AIInference# #neocloud#

Building on our earlier introduction to @nvidia #OpenShell# and our role as an official inference provider, today we’d examine how it powers safer, production-ready autonomous #agents#. In our latest blog, we’ll explore: 🔹What NVIDIA OpenShell is and why it matters for long-running, autonomous agents 🔹How its architecture enables safer and more controlled agent execution 🔹Real-world use cases and deployment approaches for agent systems As AI moves beyond experimentation toward persistent, agent-driven systems, the focus is shifting from models to how these systems are executed, governed, and scaled. Read the full blog to learn more 🔗: #AgenticAI# #AIAgents# #neocloud#

Memory Genesis Competition 2026 is in last call — submissions close on March 15. You're also welcome to join us on April 4 at the Computer History Museum for an in-person gathering and high-signal conversations with the EverMind core team and leaders across OpenAI, AWS, research institutes, open-source communities, and the investment world. Guess who will you meet? Follow the competition website for the latest updates: #AIMemory# #AgentMemory# #EverMemOS# #AgenticAI# #Hackathon# #Developers# #AIInfra#

Agentic AI is quickly becoming a core driver of modern partner ecosystems & Dell is moving early to redefine the experience. Denise Millard said it plainly: “This is really redefining an agentic partner experience across every stage of the engagement with our partners.”

Agentic AI is redefining banking across transactions, risk control, and decision-making. The shift demands more agile, resilient, and secure infrastructure. Join the Huawei Intelligent Finance Summit 2026 in Shanghai, May 20–21 to examine the next phase of agentic banking.

Agentic AI is changing the infrastructure equation. As AI moves from simple responses to planning and workflow execution, the CPU-to-GPU ratio is shifting from 1:8 or 1:4 toward 1:1. Enterprises need new CPU compute layers alongside GPU infrastructure to handle the orchestration and tool execution that agents demand. See how AMD EPYC is built for the shift:

The Underpriced Truth: Agentic AI Is a Paradigm Shift Centered on Memory 1/ The market will slowly realize: Agentic AI is memory-centric, not compute-centric. The new hardware stack is: ① Memory — HBM / DRAM / NAND ② Parallel compute — GPU / ASIC ③ Coordinator — CPU CPUs stopped doing the heavy lifting a long time ago. This isn't a cycle. It's a paradigm. 🧵👇 2/ First principles Humanity's ultimate pursuit of intelligence has always been two things: Infinite memory + infinite compute. When we say someone is smart, we mean two things: "good memory" + "fast thinking." Machine intelligence is walking the exact same path. 3/ The story the market already understands: HBM LLM inference's decode stage is a textbook memory-bound workload. Every token generated → drag the entire KV cache across memory. Bandwidth too low → expensive GPUs sit idle. That's why every new GPU generation ships with more HBM bandwidth and capacity. 4/ The story the market is missing The "1M context" you keep hearing about? It is not assembled inside the GPU inference cluster. So where is it actually built? 5/ It's built on the traditional servers running the agentic system Those CPU + huge-DRAM servers are quietly doing the heaviest lifting: • loading user long-term & short-term memory • loading the agent's system spec / prompt • loading skill / tool / subagent definitions • compressing the context once it overflows 1M tokens All of this lives in DRAM, not HBM. 6/ Compare this to the previous era In the web / mobile era, we barely stored any user context at all. Only search / recsys / ads kept a small user profile — maybe 1/20, even 1/100 of the data volume an agentic system needs today. That asymmetry is the real overlooked inflection point. 7/ The supply chain is already telling this story Server CPU : DRAM ratio is climbing fast: • Web / Mobile era: 1 core : 4 GB • Agentic AI today: 1 core : 16 GB • Deep agentic future: 1 core : 64 GB and beyond 8/ And it's NOT just "4x more memory" Under agentic workloads, a single CPU serves a fraction of the users it used to. When the entire IT stack migrates to agentic: • CPU count grows several-fold to ~10x • DRAM total grows tens-fold to ~100x That's the part nobody is pricing in. 9/ The conclusion Agentic AI is a paradigm shift centered on storage + parallel compute. The software paradigm changed. The hardware paradigm changed with it. Only those who deeply understand the technology will see it: This isn't a memory cycle. It's a memory paradigm. 10/ Time horizon Given how early we still are on: • user adoption rate • depth of usage per user We are at least 5 years away from the cyclical top of this memory wave. (Zoom out far enough and everything is a cycle — but this one is nowhere near peak.) $MU $DRAM $SNDK