'Inference Is Bigger Than Any One Chip' - d-Matrix CEO on GigaIO Deal A push to build more complete, rack-scale AI inference systems is driving d-Matrix’s acquisition of GigaIO’s data center business. The deal adds interconnect technology and systems expertise as competition shifts beyond individual chips. The deal builds on a collaboration that began in 2025 and is aimed at strengthening the company’s ability to deliver system-level AI infrastructure rather than discrete silicon. Expanding Beyond the Chip With the acquisition, GigaIO’s data center technologies, which include its SuperNode platform and FabreX PCIe-based memory fabric, are being integrated into the broader d-Matrix inference stack. The move extends a platform that already includes Corsair inference accelerators, JetStream networking, Aviator software, and the SquadRack rack-scale reference architecture developed with Broadcom and Arista. “Inference is bigger than any one chip. It’s now a systems problem,” said Sid Sheth, founder and CEO of d-Matrix. “To keep up with surging AI demand, workloads are increasingly disaggregated across CPUs, GPUs, and inference accelerators. That means data must move efficiently across chips, nodes, racks, and entire data centers in real time.” He said the acquisition aims to accelerate delivery of low-latency, efficient, and scalable infrastructure. Deal Structure and Strategic Rationale The transaction is structured as a business unit acquisition, with ownership...

'Inference Is Bigger Than Any One Chip' - d-Matrix CEO on GigaIO Deal
->Data Center Knowledge | More on "AI inference rack-scale chip acquisition" at BigEarthData.ai | #Inference #ArtificialIntelligence

Designing for the AI Era: The New Realities of Data Center Infrastructure To keep pace with AI and high‑density computing, data centers must embrace hybrid cooling architectures, prepare for HVDC ecosystems, and rethink supply‑chain and grid dependencies...

Tom Carroll (ebm-papst) in DCF Voices of the Industry:
AI is collapsing data center design into one problem: power, cooling, supply chain. Hybrid cooling is here.
Every watt saved = more compute.

www.datacenterfrontier.com/sponsored/ar...

#datacenters #AIinfrastructure #LLM #GPU #cloud #inference

Online Reasoning Calibration: Test-Time Training Enables... While test-time scaling has enabled large language models to solve highly difficult tasks, state-of-the-art results come at exorbitant compute costs. These inefficiencies can be attributed to the...

Every inference provider running “thinking” models is about to get crushed by GPU costs. Cut inference compute nearly in half with zero quality loss? That’s not a novelty.
That’s a pricing moat.
Paper: arxiv.org/abs/2604.0...
#AI #LLM #Inference #ORCA

Streamlit

Want to explore the MLPerf Inference v6.0 results yourself? Dive into our interactive dashboard - filter by benchmark, system, and scenario to see how the latest hardware stacks up. 📊

🔗 https://bit.ly/3PLbCJR

#MLPerf #MLCommons #AI #inference

APEX MoE and TurboQuant Deliver 33% Faster LLM Inference APEX MoE and TurboQuant quantization techniques deliver 33% faster LLM inference and 10% smaller model sizes, enabling local deployment on consumer GPUs.

APEX MoE quantized models now run 33% faster. TurboQuant cuts 10% off model size while maintaining Q4_0 quality. Qwen3.5-27B now fits on 16GB GPUs. Local inference just got a major boost. #llm #quantization #inference

bymachine.news/apex-moe-turboquant-33-p...

Как я взломал «исходный код» Вселенной на MacBook Pro и закрыл 90% экзопланет для жизни Пока теоретики десятилети...

#Экзопланеты #JWST #Астробиология #Bayesian #Inference #MCMC #белые #карлики #Фундаментальные #константы #Open

Origin | Interest | Match

What's new in MLPerf Inference v6.0? Watch the press briefing for a walkthrough of five new benchmarks, standout results, and what it all means for the future of AI inference. ▶️

🔗 https://youtu.be/3FdkYZZlhDI

#MLPerf #MLCommons #AI #inference

Rebellions Raises $400M to Scale AI Inference, Targets US Expansion Rebellions has raised $400 million in a pre-IPO funding round as the AI infrastructure market pivots from model training to a new bottleneck – the efficiency of running those models in production. The South Korea–based AI hardware startup is betting that inference will define the next phase of AI adoption, where power constraints, cost-efficiency, and deployment timelines outweigh raw compute performance. The round, led by Mirae Asset Financial Group with participation from the Korea National Growth Fund, brings the company’s total funding to $850 million and values it at roughly $2.34 billion. Rebellions’ rapid fundraising haul ($650 million in just the last six months) signals rapidly rising investor interest in inference. “AI is now measured by its ability to operate in the real world – at scale, under power constraints, and with clear economic return,” said Sunghyun Park, co-founder and CEO of Rebellions. “That shifts the center of gravity toward inference infrastructure and software that makes that infrastructure usable.” Inference Takes Center Stage Rebellions, whose core products include the Rebel-Quad and Atom AI accelerators, is betting that the next phase of AI adoption will be defined less by training breakthroughs and more by how efficiently models can be served in...

Rebellions Raises $400M to Scale AI Inference, Targets US Expansion
->Data Center Knowledge | More on "AI inference chip startup funding" at BigEarthData.ai | #AI #ArtificialIntelligence #Inference #Rebellion

IT University of Copenhagen Postdoc positions in Data Science for epidemic preparedness The NERDS (NEtwoRks, Data, and Society) team at the IT University of Copenhagen welcomes applications from aspiring postdocs in the areas of Data Science, Mathematical Modeling, and Network Science wi...

IT University of Copenhagen | Postdoc position in Data Science for epidemic preparedness

Application deadline: April 12

#Postdoc #Job #DataScience #Epidemics #Inference #Forecasting #NetworkScience ./8

www.complexitycat.org/posts/postdo...

IT University of Copenhagen PhD position in Data Science for epidemic preparedness The NERDS (NEtwoRks, Data, and Society) team at the IT University of Copenhagen welcomes applications from aspiring PhD students in the areas of Data Science, Mathematical Modeling, and Network Scienc...

IT University of Copenhagen | PhD position in Data Science for epidemic preparedness

Application deadline: April 12

#PhD #DataScience #Epidemics #Inference #Forecasting #NetworkScience ./7

www.complexitycat.org/posts/PhD-IT...

Non-parametric tests don’t have to be painful.

Kruskal-Wallis & Mann-Whitney U — quick, no setup, no coding.

#statistics #DataScience #analytics #nonparametric #inferentialstatistics #inference #collegestatistics #hypothesistesting #biostatistics #statisticalanalysis #nonparametrictests

Add BF16 GEMM support (mixed precision) by gicrisf · Pull Request #40 · sarah-quinones/gemm Summary This PR adds support for BF16 (bfloat16) matrix multiplication. The implementation stores inputs/outputs as BF16 but performs computation in F32, converting during the packing phase. This a...

After A LOT of studying BLAS internals, my PR to the gemm crate is finally open (optimal for use cases like small models doing autoregressive decoding on CPU)

github.com/sarah-quinon...

#programming #rust #ai #inference #deeplearning #qwen #asr #opensource #rustlang

AI's infrastructure crunch: Inside CNCF's play to bring order to inference chaos Unpredictable demand, specialized hardware, production-scale complexity — all of it is making AI inference harder to run at enterprise scale. Now, cloud-native open-source infrastructure is emerging as the answer to inference chaos. That shift is already showing up in the Kubernetes ecosystem. In fact, the Cloud Native Computing Foundation has almost doubled the number of approved platforms in its Kubernetes AI Conformance Program, following an over 70% surge in certified offerings, according to Jonathan Bryce (pictured), executive director of cloud and infrastructure at the Linux Foundation. The program creates open, community-defined standards for running AI workloads on Kubernetes, and as organizations increasingly move those workloads into production, they need consistent and interoperable infrastructure. “AI is going to be something that is [going to] drive the next 10, 20 years of technology, the way that cloud did the last 10 or 20 years,” Bryce told theCUBE, SiliconANGLE Media’s livestreaming studio. “But it’s also a very different workload than what we’ve ever run before. It requires specialized hardware. The usage patterns are super unpredictable … When you talk about bursty [demand spikes] and AI, you could need a thousand times as much capacity and then it goes away.” Bryce spoke with theCUBE’s...

AI's infrastructure crunch: Inside CNCF's play to bring order to inference chaos
->SiliconANGLE | More on "AI inference Kubernetes cloud infrastructure" at BigEarthData.ai | #AI #ArtificialIntelligence #Inference

Maxwell's daemon, the Turing machine, and Jaynes' robot A review of Jaynes' posthumous book "Probability Theory--The Logic of Science." I use scientific and personality elements gathered from other papers by Jaynes to help throw light on the origins of Jay...

Tommaso Toffoli
Maxwell's daemon, the Turing machine, and Jaynes' robot
2004

#bookreview #logic #science #probability #probabilitytheory #bayesian #inference #reasoning #maxent #philosophy #updateyourpriors

AI Inference: The Next Stress Test for Global Data Center Infrastructure In recent years, AI training has dominated conversations around the global artificial intelligence infrastructure. Massive GPU clusters, data center buildout, and power-hungry models have become shorthand for the scale of the AI era. But AI training is only the warmup act. AI inference, the real test of today’s AI infrastructure, has been waiting in the wings and is now taking center stage. As AI becomes more multimodal and more deeply embedded across digital platforms, inference is emerging as a dominant driver of future network demand. It is also fundamentally shifting how data centers operate globally. To cope with surging inference workloads, the industry must address the critical, yet often overlooked, bottleneck of the network – the optical connectivity that ties the entire fabric together. Growing AI Inference Workloads AI inference is the ‘doing’ phase of the AI model lifecycle. It is when a trained model can process unseen data to provide an answer, generate an image, or carry out a task. Unlike training, which is a highly localized process, inference happens everywhere – across applications, enterprises, and consumer devices. And inference workloads are multiplying as AI adoption surges. While it’s taken decades for previous technologies or digital platforms to be...

AI Inference: The Next Stress Test for Global Data Center Infrastructure
->Data Center Knowledge | More on "AI inference data center infrastructure" at BigEarthData.ai | #AI #ArtificialIntelligence #Data #Inference

Original post on mastodon.xyz

“A sophisticated semantic network system capable of encoding #inference rules within the network itself. Built for efficient memory usage and powerful logical #reasoning, zelph can process the entire #Wikidata knowledge graph (1.7TB) to detect contradictions and make logical deductions.” […]

Solving Memory Shortages with SSDs! The Revolutionary LLM Scheduler Solving Memory Shortages with SSDs! The Revolutionary LLM Scheduler

[JP] メモリ不足をSSDで解決！Apple Silicon専用のLLMスケジューラ「Hypura」が革命的
[EN] Solving Memory Shortages with SSDs! The Revolutionary LLM Scheduler

ai-minor.com/blog/en/2026-03-25-17743...

#AppleSilicon #LLM #Inference #OpenSource #AI #Tech

Gimlet Labs secures $80M to revolutionize AI inference with its multi-silicon cloud, optimizing workloads across diverse hardware for enhanced efficiency. #AI #Inference #TechInnovation Link: thedailytechfeed.com/gimlet-labs-...

Original post on webpronews.com

The Chip Startup That Wants to Be the Air Traffic Controller for AI Inference Israeli startup NeuReality, backed by former Google AI infrastructure chief Amin Vahdat, is building a purpose-built ch...

#AITrends #AI #inference #chip #Amin #Vahdat #Data #Center […]

[Original post on webpronews.com]

Deformation Quantization of Distributed Inference: The Convex Case Using modest spectral graph theory, we show that under the assumption of convexity, beliefs will diffuse towards consensus. Our toy model captures opinion dynamics in a manner sensitive to the order o...

#math #inference #belief #graph

#topology

#statstab #511 Seven Myths of Randomisation
in Clinical Trials

Thoughts: Randomization is a very power tool for inference. Closest we have to magic in research. But it's also misunderstood.

#randomization #experiment #inference #design #bias #science

www.methodologyhubs.mrc.ac.uk/files/9214/3...

Jensen Huang Maps the AI Factory Era at NVIDIA GTC 2026 From the “inference inflection point” to OpenClaw’s rise as an agent operating system, Nvidia’s GTC keynote outlined the architecture of the AI factory, spanning Rubin systems...

The #AI story now jumps from training #LLMs to running them continuously at planetary scale. From the #inference inflection point to #AIFactories producing #tokens like a #commodity, #JensenHuang sees a trillion-dollar #infrastructure buildout approaching.

www.datacenterfrontier.com/machine-lear...

Original post on webpronews.com

Mozilla’s Llamafile Hits Version 0.10: The Single-File AI Runtime That Keeps Getting Faster Mozilla's Llamafile 0.10 delivers faster local AI inference, broader model support, and improved st...

#AIDeveloper #Justine #Tunney #Llamafile #0.10 #local #AI […]

[Original post on webpronews.com]

India's AI moment will be decided at the inference layer In the last few years, AI has quietly moved from the lab to the balance sheet. McKinsey’s latest global survey finds that 72% of organisations now use AI in at least one business function, and 65% are already using generative AI regularly – roughly double the share in 2023. Menlo Ventures reports that enterprise gen-AI spending jumped from $2.3 billion in 2023 to $13.8 billion in 2024, a six-fold surge as companies shift from pilots to production and pour money into real deployments, especially at the application layer. As AI becomes embedded in a variety of domains, the real contest is no longer about who can train the largest model. It is about which models you run, for which sector, on whose infrastructure. That is the inference layer – the part of the stack where models are served, make decisions, and answer the real questions – and that is why verticalised AI really starts to matter. Verticalised AI: From generic intelligence to sector fluency Gartner reports that enterprise adoption is shifting from experiments with generic LLMs to domain-specific generative AI tailored to particular industries and functions. Venture investors like Bessemer describe “Vertical AI” as the future – AI built ground-up...

India's AI moment will be decided at the inference layer
->Financial Express | More on "India AI inference layer strategy" at BigEarthData.ai | #ArtificialIntelligence #Inference #AI

Original post on webpronews.com

The Vanishing Cost of Intelligence: Why Box’s Aaron Levie Thinks AI Will Be Nearly Free by 2026 Box CEO Aaron Levie predicts AI token costs will approach zero by 2026, a claim with massive implic...

#AITrends #CloudWorkPro #Aaron #Levie #Box #AI #inference […]

[Original post on webpronews.com]

#FEP #Active #Inference
🔓 Murata, S. (2026). Free-energy principle and predictive coding: A computational theory explaining various brain functions. In T. Taniguchi (Ed.), Symbol emergence systems (pp. 85–90). Springer. doi.org/10.1007/978-...

Original post on flipboard.social

The term inference has been making the rounds recently, especially since Nvidia's recent (love it or hate it) conference. WSJ explains what inference is, why Big Tech companies are shifting toward it and what implications it could have on big and small tech companies.

Gift link […]

New #J2C Certification:

Statistical Inference for Generative Model Comparison

Zijun Gao, Han Su, Yan Sun

https://openreview.net/forum?id=PXL6SBxh0q

#generative #inference #benchmark

NVIDIA, Telecom Leaders Build AI Grids to Optimize Inference on Distributed Networks As AI‑native applications scale to more users, agents and devices, the telecommunications network is becoming the next frontier for distributing AI. At NVIDIA GTC 2026, leading operators in the U.S. and Asia showed that this shift is underway, announcing AI grids — geographically distributed and interconnected AI infrastructure — using their network footprint to power and monetize new AI services across the distributed edge. Different operators are taking different paths. Many are starting by lighting up existing wired edge sites as AI grids they can monetize today. Others harness AI-RAN — a technology that enables the full integration of AI into the radio access network — as a workload and edge inference platform on the same grid. Telcos and distributed cloud providers run some of the most expansive infrastructure in the world: about 100,000 distributed network data centers worldwide, spanning regional hubs, mobile switching offices and central offices, with enough spare power to offer more than 100 gigawatts of new AI capacity over time. AI grids turn this existing real-estate, power and connectivity into a geographically distributed computing platform that runs AI inference closer to users, devices and data, where response and cost per token align best. This is more...

NVIDIA, Telecom Leaders Build AI Grids to Optimize Inference on Distributed Networks
->NVIDIA Blog | More on "AI grids telecom distributed networks" at BigEarthData.ai | #ArtificialIntelligence #Inference #AI