VAST Data & NVIDIA Unify AI Infrastructure with CNode-X

A defining moment in the age of data computing. VAST Data is closer that ever in getting closer in building a thinking machine. The collaboraion between VAST & NVIDIA in bringing a new ages of software infrastructure is just epic. #inference #AI #software #AcceleratedComputing

 Turning "Slow fdatasync" into a lesson in Infrastructure Resiliency. 🛠️ Lately, my home lab has been my greatest teacher. What started as a project to pass an NVIDIA GTX 1660 Ti through to a Kubernetes (Official) cluster turned into a deep dive into etcd latency and CNI architecture. Running K3s on Proxmox Server Solutions presents unique challenges—especially when you hit those dreaded HTTP 429 and slow fdatasync errors. It’s a reminder that high-level MLSecOps isn't just about the models; it's about the stability of the ground they run on. There’s nothing quite like debugging a "Stopped" module in a CNI to make you appreciate the complexities of cloud-native infrastructure. This week’s project has been a wild ride of: ✅ Repurposing legacy HP ProDesk hardware. ✅ Tuning VXLAN MTUs to play nice with OpenWrt. ✅ Learning exactly why etcd needs a fast SSD. Key Takeaways from the lab this week: etcd is King: Disk latency is the silent killer of control planes. eBPF Summit Power: Moving to Cilium with KubeProxyReplacement drastically changes the networking game in a virtualized environment. Infrastructure Matters: You can't secure an ML pipeline if your underlying storage IOPS are saturated. Building a heterogeneous cluster with HP ProDesk and Lenovo Legion nodes has been a masterclass in resource optimization. Onward to the next deployment! #Kubernetes #K3s #Cilium #MLSecOps #HomeLab #Proxmox #SystemsEngineering #Infrastructure #etcd

x86 vs ARM CPUs Data centers are evolving fast, and it’s no longer just about speed. Power efficiency, AI workloads, and long-term CPU availability are reshaping infrastructure decisions in 2026. Which architecture is actually gaining ground? Read the full article to find out: https://lnkd.in/exTh-wh7 • • • #datacenters #x86 #arm #aiinfrasctucture #microchipusa #longtermsupplier #electroniccomponents #usasupplier

🚀 NVIDIA just showed the future of inference at GTC 2026 — and it’s heterogeneous. Jensen Huang unveiled the Groq 3 LPX platform today: 128 LPUs co-designed with Vera Rubin NVL72 GPUs, delivering 35× higher throughput per megawatt. The message is clear — the next era of inference is about combining different chip architectures, each doing what it does best. And real-world datacenters go even further. Most clusters already hold GPUs from multiple vendors, generations, and architectures — NVIDIA alongside AMD, last-gen nodes next to new deployments. The opportunity is enormous, but traditional inference software can’t serve them together. 🧩 That’s what MoAI Inference Framework was built for. Moreh’s software unifies NVIDIA, AMD, Tenstorrent, and legacy GPUs into a single inference cluster behind one API — splitting prefill and decode across vendors, routing workloads intelligently, and keeping every GPU productive. ⚡ The heterogeneous compute era is here. We make sure your whole fleet is part of it. 🔥 🔗 https://lnkd.in/gWi2RiZy #GTC2026 #NVIDIA #AIInference #HeterogeneousComputing #LLMInference #MLInfrastructure #AIInfrastructure #GPU #Moreh #MoAI

Spent some time recently understanding how Kubernetes actually uses CPU under the hood, and I wanted to share a simple breakdown of what I learned.

Introducing GLYPH — a clean-slate computer architecture built for high-performance emulation, virtualization, and portable deployment. A standout feature is an integrated network-on-chip that supports reliable multicast for thread-group signaling. It is designed to map closely to Intel/AMD x86, with an expanded register file and a software-defined MMU. 📂 Repo: https://lnkd.in/ec7c9cSj 📘 Doc: https://lnkd.in/eZdERC_R #ComputerArchitecture #VirtualMachines #Emulation #SystemsDesign

When AI is part of a server refresh conversation, a few technical realities start to matter more than they used to: • AI workloads move large volumes of data, often exposing memory and I/O bottlenecks before CPU limits • CPUs can sit underutilized if the platform can’t move data fast enough to accelerators • Adding GPUs only helps if the surrounding architecture can keep them fed • Power and cooling constraints on older servers can cap density sooner than expected • Platform-level security and firmware integrity become more relevant as stacks grow more complex This VLCM blog breaks down how modern server platforms are being designed around these considerations, using HPE ProLiant Gen11 and Gen12 as examples of where compute is heading as AI workloads become more common. Read the post: https://lnkd.in/gR_F2xni

At the top level, a GPU chip is made up of many Streaming Multiprocessors (SMs). Think of SMs as mini parallel engines replicated across the chip. Instead of one big brain, you get dozens of smaller ones working simultaneously. Inside each SM: 🔹A Warp Scheduler decides which group of threads (a warp) runs next. 🔹Dozens of CUDA Cores execute instructions in parallel. 🔹A Register File stores thread-local data at ultra-low latency. 🔹Load/Store units move data between registers and memory. 🔹Texture units handle specialized memory operations. 🔹L1 Cache provides fast, on-SM data access. Each SM works independently, but they’re connected through an on-chip interconnect. Below that sits the L2 Cache, shared across all SMs. This is the coordination layer. If one SM misses in L1, it checks L2 before going to global memory. Then come the Memory Controllers, which interface with Global Memory. This is where things get interesting: Extremely high bandwidth Much higher latency than on-chip memory That’s why GPUs rely on massive parallelism. While some threads wait on memory, thousands of others keep executing. #SystemDesign #TechSuccess #EngineeringExcellence #learning #softwaredevelopment #webdevelopment #database #servers ImgCredit: ByteByteGo

x86’s SIMD evolution is a compatibility nightmare. Every new vector width (SSE → AVX → AVX-512) requires completely different code. Libraries ship 3-4 versions of every optimized function. Your AVX-512 code runs on less than 20% of x86 CPUs. RISC-V Vector Extension solves this with length-agnostic design. Write once, run anywhere: ∙ Same binary on 128-bit embedded chips ∙ Same binary on 256-bit general-purpose cores ∙ Same binary on 512-bit HPC processors ∙ Same binary on 1024-bit supercomputers The hardware adapts automatically. No recompilation. No multiple code paths. No runtime CPU detection. Performance scales linearly with hardware width: ∙ 128-bit: 4× speedup ∙ 256-bit: 8× speedup ∙ 512-bit: 16× speedup Simpler than ARM SVE. Open standard, no vendor lock-in. Already shipping in commercial silicon (Alibaba, SiFive, SpacemiT). I wrote a complete technical breakdown covering: ∙ Application Vector Length abstraction ∙ Register grouping (LMUL) ∙ Real assembly code examples ∙ Performance benchmarks ∙ Hardware implementations What’s your experience with SIMD? AVX, NEON, or SVE? #RISCV #ComputerArchitecture #SIMD #VectorProcessing #HPC Full deep-dive:

🚀 𝗥𝗲𝗮𝗱𝘆 𝘁𝗼 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗲 𝗲𝗱𝗴𝗲 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗿𝗲𝗱𝗲𝘀𝗶𝗴𝗻𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝘀𝘆𝘀𝘁𝗲𝗺? Watch this short 𝟯-𝗺𝗶𝗻𝘂𝘁𝗲 𝘃𝗶𝗱𝗲𝗼 to see how 𝗜𝗻𝘁𝗲𝗹 𝗖𝗼𝗿𝗲 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗼𝗿𝘀 (𝗦𝗲𝗿𝗶𝗲𝘀 𝟮) enhance Premio’s #𝗥𝗖𝗢 and #𝗩𝗖𝗢 Series with stronger hybrid compute, higher sustained performance, and seamless platform compatibility. Video Highlights:  • ⚡ Higher sustained performance for demanding edge workloads  • 🔄 Drop-in upgrade from 13th Gen CPUs   • 🧠 Hybrid architecture built for mixed compute environments  • 🛡️ Rugged, long lifecycle platform continuity Watch the video: https://lnkd.in/ggPEktRv #EdgeComputing #IndustrialComputing #EdgeAI #EmbeddedSystems #RuggedComputing #AIAtTheEdge #IntelCore #EdgePerformance #IndustrialAutomation #SmartManufacturing #RealTimeComputing #PremioTech