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

Original post on blogs.nvidia.com

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 ...

#AI #Infrastructure #Artificial #Intelligence #GTC #2026 […]

[Original post on blogs.nvidia.com]

Original post on blogs.nvidia.com

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 ...

#AI #Infrastructure #Artificial #Intelligence #GTC #2026 […]

[Original post on blogs.nvidia.com]

Nvidia launches Dynamo 1.0 AI inference operating system Nvidia has commenced production of Dynamo 1.0, an open-source operating system designed for large-scale AI inference. Dynamo 1.0 is currently in use across a range of global cloud service providers, AI-native firms and enterprises. The software is available immediately to developers worldwide. Dynamo 1.0 works with the Nvidia Blackwell platform to manage GPU and memory resources for AI workloads across data centre clusters. It divides inference tasks between GPUs and uses advanced traffic management tools to move data efficiently between GPUs and storage systems, reducing memory bottlenecks and computational overheads. For agentic AI applications and processes involving lengthy prompts, the system routes requests to GPUs already containing relevant data from earlier steps, offloading this information when it becomes unnecessary. Recent benchmarks indicate that Dynamo can increase the inference performance of Blackwell GPUs by as much as seven times, reducing the operational cost per token for users employing millions of GPUs. As open-source software, Dynamo 1.0 aims to address challenges associated with scaling AI inference in data centres, where varying request sizes and unpredictable demand make resource orchestration complex. Dynamo integrates natively with leading open-source AI frameworks such as LangChain, llm-d, LMCache, SGLang and vLLM through optimisations made possible by the...

Nvidia launches Dynamo 1.0 AI inference operating system
->Tech Monitor | More on "Nvidia Dynamo AI inference software" at BigEarthData.ai | #ArtificialIntelligence #Inference #AI

Nvidia bets on AI inference as chip revenue opportunity hits US$1 trillion Nvidia said the revenue opportunity for its artificial intelligence chips may reach at least US$1 trillion through 2027, as the company outlined a strategy to compete more aggressively in the fast-growing market for running AI systems in real time. CEO Jensen Huang unveiled a new central processor and an AI system built on technology from Groq - a chip startup from which Nvidia licensed technology for $17 billion in December at its annual GTC developer conference in San Jose, California. The moves are part of Huang’s bid to firm up the company’s position in so-called inference computing, the process of answering queries, where its graphics processors face greater competition from central processing units and custom processors built by the likes of Google. Nvidia chips have dominated the process of AI model training, which has been the focus of recent years. “The inference inflection has arrived,” Huang said. “And demand just keeps on going up,” he added. Dressed in his signature black leather jacket, Huang was speaking at a hockey arena with a capacity of more than 18,000 at the four-day conference that has become one of the biggest showcases of AI technology. “I just want to remind you, this is...

Nvidia bets on AI inference as chip revenue opportunity hits US$1 trillion
->BNN Bloomberg | More on "Nvidia AI inference chip revenue" at BigEarthData.ai | #ArtificialIntelligence #Inference #AI

Samsung Unveils HBM4E AI Memory Chips at GTC 2026 in SK Hynix Supply Race Samsung has unveiled HBM4E memory at Nvidia's GTC 2026 as it races to close a supply gap with SK hynix ahead of nearly $1 trillion in projected chip orders.

winbuzzer.com/2026/03/17/s...

Samsung Unveils HBM4E AI Memory Chips at GTC 2026 in SK Hynix Supply Race

#AI #NVIDIA #Samsung #SKHynix #Semiconductors #Memory Groq #BigTech #Chipmakers #Inference #VeraRubin #HBM4 #HBM4E #GTC2026

Original post on webpronews.com

Nvidia’s GTC 2025 Reveals a New AI Systems Play — and Groq Is Racing to Beat It on Inference Nvidia's GTC 2025 revealed a full-stack AI systems strategy targeting inference workloads, while...

#AIDeveloper #AI #inference #market #Blackwell #Ultra #Groq […]

[Original post on webpronews.com]

Nvidia introduces platform for large-scale AI training and inference Nvidia Corp. today stoked the fires of the emerging artificial intelligence factory trend with the announcement of Dynamo 1.0, an open-source platform the company is positioning as an essential software layer for large-scale AI deployments. The announcement at the company’s GPU Technology Conference in San Jose is aimed at one of the most daunting problems in enterprise AI: how to run increasingly complex generative and agentic workloads efficiently at large scale. Nvidia said that the economics of inference are becoming as important as raw model performance. The company sees a rapidly expanding market for software that can manage growing AI complexity, said Ian Buck, vice president of hyperscale and high-performance computing. “As we move up the complexity scale, so does the value and the capability of the AI and the dollar per million tokens,” he said. “Software stacks like Dynamo provide an uplift for models on Vera Rubin NVL72 and achieve 10 times the throughput per watt, or one-10th the token cost.” Vera Rubin NVL72 is a new rack-scale AI supercomputer platform that Nvidia announced in January. It’s designed to handle massive-scale AI training and inference. Platforms like Dynamo are critical to Nvidia’s efforts to expand beyond chips, servers and...

Nvidia introduces platform for large-scale AI training and inference
->SiliconANGLE | More on "Nvidia AI inference platform launch" at BigEarthData.ai | #ArtificialIntelligence #Inference #AI

Jensen Huang says the next AI boom belongs to inference Jensen Huang walked onto the SAP Center stage on Monday for his GTC keynote address and did what he does best: turning a product keynote into a zoning hearing for the future. The Nvidia $NVDA founder opened GTC by promising a tour through “every single layer” of AI, then spent the next stretch arguing that the company isn’t just selling chips into a hot market. Nope. The company wants to define the whole physical plant of the AI economy: the compute, the networking, the storage, the software, the models, the factories, and — because subtlety is clearly out of season — maybe even the orbital data centers. The keynote sprayed announcements in every direction, but the real message was tighter than the confetti cannon made it look. Huang wanted investors, customers, and rivals to hear four things clearly: AI demand is still climbing fast enough to justify indecent amounts of spending; inference is now the center of the battlefield; agents are supposed to spill out of chatbots and into the daily machinery of office work; and the next gold rush after digital AI could be physical AI, where robots, autonomous systems and industrial software burn through even more data and...

Jensen Huang says the next AI boom belongs to inference
->Quartz | More on "Jensen Huang AI inference boom" at BigEarthData.ai | #ArtificialIntelligence #Inference #AI

CoreWeave Expands AI Cloud with Nvidia B300 as Inference Demand Surges CoreWeave unveiled a major expansion of its AI-native cloud platform at Nvidia's GTC this week, aimed at helping enterprises move faster from model training to production deployment, with a particular emphasis on next-generation agentic AI systems and reinforcement learning workloads. The company announced the general availability of infrastructure based on the Nvidia HGX B300 platform, alongside a suite of integrated development and monitoring capabilities built with Weights & Biases, a machine-learning experiment-tracking platform. The move reflects a broader market trend as organizations transition from large-scale training to continuous model improvement and high-volume inference. "CoreWeave’s rotation to inference – activating AI – is good to see," Matt Kimball, vice president and principal analyst at Moor Insights & Strategy, told Data Center Knowledge. "Inference, where the value of AI is realized, is just beginning to ramp and could be orders of magnitude larger than training." Kimball added, "The economic impact of AI happens during inference. Memory, interconnect bandwidth, and efficiency matter as much as raw compute." Stephen Sopko, analyst-in-residence at HyperFrame Research, said his company’s first-quarter research shows 30% of organizations have reached AI deployment at scale, with 64% expecting to do so within six months. “That's the demand wave CoreWeave is...

CoreWeave Expands AI Cloud with Nvidia B300 as Inference Demand Surges
->Data Center Knowledge | More on "CoreWeave Nvidia AI cloud inference" at BigEarthData.ai | #ArtificialIntelligence #Inference #AI

Nvidia CEO Jensen Huang seemingly 'realises' that Google, Microsoft and Meta are set to eat the company's lunch - The Times of India Tech News News: Jensen Huang built Nvidia into a $4.5 trillion empire on a deceptively simple premise: one chip, every workload, everywhere. For years, it worked spec.

Bonne nouvelle, les couts d'inférence vont baisser timesofindia.indiatimes.com/technology/t... #ia #intelligenceartificielle #inference #nvidia

#105 - AI Agents Are Replacing Your To-Do List — And Your 9-to-5 Autonomous AI agents are no longer a futuristic concept — they're executing tasks, managing workflows, and generating passive income right now. This episode breaks down how Agentic RAG and multi-agent orchestration are collapsing the traditional workday into hours, not days. You'll discover how early adopters are deploying agent stacks that run 24/7 without supervision. The question isn't whether AI agents will change your work — it's whether you'll be the one holding the controls.

📣 New Podcast! "#105 - AI Agents Are Replacing Your To-Do List — And Your 9-to-5" on @Spreaker #agentic #agents #ai #automation #autonomous #blockchain #crypto #defai #income #inference #orchestration #passive #productivity #protocol #rag #solopreneur #tokenization #virtuals #web3 #workflow