Supermicro and NVIDIA set new STAC-ML benchmark records

Imagine being in a "Dream" and your Holy Guardian Agel or DAEMON says " Your AI/GPU processor will: • Outperform NVIDIA H100 in AI benchmarks • Match/exceed AMD MI300X memory capacity   • Provide better price/performance than competitors • Enable new AI applications with unified architecture" What will you do? about Algorithmic Intelligent Microprocessors?

AMD Instinct and DriveNets Ethernet Fabric https://lnkd.in/dKjVqVZR AI innovation depends not just on raw GPU power, but on the fabric that ties GPUs together. The DriveNets Network Cloud-AI networking fabric solution delivers the highest performance AI connectivity for any GPU, NIC or optics, based on lossless, Ethernet-based backend fabric. In this post, Sani Ronen explains how the DriveNets solution enables the industry’s fastest time-to-first-token (TTFT) and lowest cost per million tokens for AI clusters based on AMD Instinct GPUs.

Is the "chip war" a misnomer? By 2025, the real battle is the "packaging war." We're all focused on GPU design, but what if NVIDIA's real uncopyable moat isn't the logic chip, but the advanced package it sits on? The "monolithic" chip is dead. The future is "chiplets." And the value has shifted from designing the chip to connecting them at scale. Technologies like TSMC's CoWoS are the new strategic high ground. Is your supply chain strategy still focused on the wrong war? 👉 Full analysis of this architectural shift: https://lnkd.in/g8q3HmzE #AdvancedPackaging #Chiplets #AIChips #Semiconductors #NVIDIA #TSMC #CoWoS #Strategy

China blocks Nvidia’s RTX Pro 6000D AI chips 🚫🔌 – a move to strengthen domestic semiconductor power and cut reliance on foreign tech. 🇨🇳💻

NVIDIA breaking records seems to be a recurrent theme. Well, this one is very close to home as it relates to NVIDIA GH200 Grace Hopper Superchip ability to stream financial data, and power DL models that forecast market behavior in real time. Full report here: https://bit.ly/47nm94d STAC recently audited a STAC-ML™ Markets (Inference) benchmark on a stack featuring NVIDIA GH200 Grace Hopper Superchip on Supermicro. Compared to the previous FPGA-based record, GH200 delivered: ⚡ Up to 49% lower latency on large models 🔋 44% higher energy efficiency 📉 8–13× lower inference error rates ⏱️ Latency as low as 4.67 µs (99p)

I couldn’t shake the feeling that something deeper is happening with the AMD-OpenAI power move . It’s easy to see it as another chip partnership, but when you dig into the details, it feels like the beginning of a real power shift. For years, AMD stood as the capable challenger strong in hardware, but always on the outside of NVIDIA’s CUDA-powered software world. What changed this time is alignment: AMD worked hand in hand with OpenAI to make its GPUs run Triton seamlessly. That’s not a technical footnote; that’s a liberation moment. With a staggering 6 gigawatts. We’re entering an era where computing power is measured not just in FLOPS or cores, but in energy consumed per unit of intelligence created. Turning power into intelligence most efficiently is the new frontier . This deal isn’t only about beating NVIDIA. It’s about changing the metric of progress itself, from raw performance to integrated purpose, from chip volumes to energy intelligence. Source is in the comments.

The AMD–OpenAI deal marks a direct challenge to Nvidia’s dominance by coupling AMD’s next-gen MI450 AI chips with a strong software integration layer co-developed alongside OpenAI—something Nvidia has long leveraged through CUDA. Unlike Nvidia’s closed, vertically controlled ecosystem, AMD is pursuing an open and collaborative model that allows partners like OpenAI to shape performance optimization and deployment at scale. This could gradually erode Nvidia’s lock-in advantage if AMD delivers comparable performance and a mature software stack. However, Nvidia still holds a major lead in ecosystem maturity, developer adoption, and production capacity, meaning AMD’s success will hinge on execution speed and the real-world efficiency of its new hardware–software co-design once large-scale deployments begin in 2026.

The #HBM4 era is characterized by fierce competition, with SK hynix anticipated to lead the market share in 2025. Key players like NVIDIA and AMD are driving demand for HBM4 in their next-generation #GPUs. JEDEC specifications for HBM4 include a relaxed package thickness and doubled I/O counts compared to previous generations, promising significantly higher data throughput.

🤔 Micron is the world’s first memory maker delivering both HBM3E and SOCAMM—but can it hold the lead? Rumors say NVIDIA may split next-gen SOCAMM2 orders evenly across the top three memory makers, powering Rubin. 🔎 Unlike HBM, SOCAMM reportedly shows only minor differences across suppliers, shifting the focus to cost efficiency—driven largely by next-gen 10nm-class (1c) processes. 💡More: https://buff.ly/OKezYbP 🔗 #SOCAMM #Micron #Samsung #SKhynix

The Storage Networking Industry Association (SNIA) has launched the Storage.AI initiative, an open standards project in collaboration with leading tech companies including AMD, Intel, and Samsung. The initiative aims to optimize end-to-end data flow for AI workloads by defining universal interfaces such as the memory-semantic SDXI, enabling seamless coordination across GPUs, SSDs, and network infrastructure. By breaking down "data silos," Storage.AI seeks to reduce GPU idle time by 30-50%, significantly improve computational efficiency in AI training and inference, lower integration complexity, and establish a vendor-neutral ecosystem for scalable and cost-effective AI infrastructure.