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TSMC LEADING NODE YIELD2nmMass production 2026
AI SILICONAmazon Releases Open-Source TorchNeuron to Take on NVIDIA

Amazon Releases Open-Source TorchNeuron to Take on NVIDIA

Seeking to erode NVIDIA Corp.’s dominance in artificial intelligence (AI), Amazon.com Inc. has introduced a new software tool designed to allow AI models to run on its custom chips with minimal code modifications. The software, named TorchNeuron, lets developers execute code written in PyTorch, the industry’s most popular open-source AI framework, on Amazon’s proprietary Trainium […]

Jon Swartz · Oct 20, 2026 · 3 min
AI SILICONSamsung Taps TSMC to Build NVIDIA’s Next-Generation AI Memory Chips

Samsung Taps TSMC to Build NVIDIA’s Next-Generation AI Memory Chips

Samsung Electronics plans to use rival chipmaker TSMC’s advanced manufacturing technology to produce a key component of NVIDIA’s next-generation AI memory chips, a major change in Samsung’s semiconductor strategy. The agreement, first reported by the Seoul Economic Daily, is part of NVIDIA’s effort to improve the performance and supply of high-bandwidth memory, a crucial technology […]

James Maguire · Oct 19, 2026 · 3 min
AI INFRASTRUCTUREChina Launches 100,000-Card AI Supercluster Built with Domestic Technology

China Launches 100,000-Card AI Supercluster Built with Domestic Technology

Chinese supercomputer manufacturer Sugon Information Industry has launched a massive AI supercluster powered by more than 100,000 domestically developed computing cards, a major advance in China’s effort to build its own high-performance computing infrastructure. The system, called Sugon 8000 or Dawning 8000, is operating in Zhengzhou, Henan province. The supercluster is a major step up […]

James Maguire · Oct 19, 2026 · 2 min
World-class semiconductor analysts delivering institutional-grade intelligence to Techstrong Semi readers

Meet the Analysts

Brendan Burke headshot

Brendan Burke

Research Director, Semiconductors

Leads Futurum's Semiconductors Practice covering edge AI, generative AI, and hyperscale silicon architectures.

Nick Patience headshot

Nick Patience

VP & Practice Lead, AI Platforms

25+ years covering AI and enterprise software. Co-founded 451 Research. London-based analyst covering AI platforms and infrastructure.

Olivier Blanchard headshot

Olivier Blanchard

Research Director, Intelligent Devices

Covers intelligent devices, edge AI, sustainability, and the convergence of silicon and software at the edge.

Keith Kirkpatrick headshot

Keith Kirkpatrick

VP & Research Director, Enterprise Software

Covers enterprise applications, digital workflows, and the intersection of AI with enterprise software strategies.

Meet the full analyst team →

Deep Analysis

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01
MEMORY & STORAGE

Quantum Computing Will Break AI’s Science Barrier and Open a $1.5 Trillion Addressable Market by 2040

The scientific barriers quantum will break, the TAM each one opens, and the use cases AMD, AWS, Google, IBM, IonQ, Microsoft, and NVIDIA are targeting.

admin-perplexity·Oct 30, 2026·10 min
02
ANALYSIS

Oracle Makes the Supercluster Multi-Silicon at AI World 2026

Oracle expands NVIDIA Vera Rubin and adds Google TPUs, Cerebras, SambaNova, and Intel Diamond Rapids to OCI’s multi-silicon supercluster.

admin-perplexity·Oct 29, 2026·6 min
03
AI SILICON

Enterprises Embrace Open Weights but Infrastructure Sellers Profit Most

Futurum data show open-weight models now carry a third of production AI tokens, with Google, Microsoft, and NVIDIA capturing most of the gains.

admin-perplexity·Oct 28, 2026·9 min
04
ANALYSIS

NVIDIA’s 104-Qubit JUPITER Simulation Makes GPU Supercomputers the Design Platform for Fault-Tolerant Quantum

NVIDIA and Jülich run a record 104-qubit fault-tolerant quantum simulation on JUPITER, validating magic state cultivation with 90% fewer physical qubits.

admin-perplexity·Oct 22, 2026·6 min

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View All Events →
JUN4
WEBINAR

From Experimentation to Production: Why Inference Is the Defining Layer of AI

CoreWeave-sponsored webinar examining why operationalizing AI inference at scale — not training new models — is the real challenge facing enterprises in 2026.

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MAY1
WEBINAR

Breaking the Wall: How CharacterAI Scaled AI Inference to Millions with AMD and DigitalOcean

On-demand webinar with CharacterAI’s Chief Architect on how AMD MI325X accelerators with 256GB HBM and DigitalOcean enabled production-scale inference on half the hardware.

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MAY20
WEBINAR

Bare Metal at AI Speed

RackN webinar on building AI infrastructure automation for bare metal at scale — covering operator challenges and high-speed, large-scale bare metal management with AIOps.

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MAY27
WEBINAR

Beyond Embedded! RISC-V in the Performance CPU segment

RISC-V is commonly seen as an ISA for embedded products; for smaller devices that require low power consumption but aren’t very complex and can tolerate lower performance. SiFive, a leading RISC-V IP vendor, is proving that incorrect with its Performance family of products. They recently launched the latest product in the series, the P550, that supports the RVA23 profile: a crucial enabler for SW developers and the industry as a whole. In this webinar, we discuss how SiFive has been able to deli

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MAY27
WEBINAR

The AI Reality Check – Operationalizing for Business Value

There has never been a megatrend as transformative as AI. 2023 unlocked new possibilities, 2024 brought new proof of concepts and 2025 was the year to prove the ROI and business value generation of AI. 2026 is the year of the AI reality check and organizations that successfully operationalize AI to drive concrete efficiency and measurable impact will be the ultimate winners.

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JUN4
WEBINAR

From Experimentation to Production: Why Inference Is the Defining Layer of AI

As AI adoption accelerates, organizations are discovering that the real challenge is no longer building models—it’s running them reliably at scale. This webinar explores the growing “production AI gap,” where promising prototypes fail to translate into consistent, real-world outcomes. Attendees will gain a clearer understanding of why inference—not training—is becoming the defining layer of AI, and what it takes to operationalize AI systems in environments shaped by unpredictable demand, latency

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