Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Researchers have introduced Light-R1-32B, a new open-source AI model optimized to solve advanced math problems. It is now available on Hugging Face under a permissive Apache 2.0 license — free for enterprises and researchers to take, deploy, fine-tune or modify as they wish, even for commercial purposes. The 32-billion parameter (number of model settings) model surpasses the performance of similarly sized (and even larger) open-source models such as DeepSeek-R1-Distill-Llama-70B and DeepSeek-R1-Distill-Qwen-32B on the third-party American Invitational Mathematics Examination (AIME) benchmark that contains 15 math problems designed for extremely advanced students and has an allotted time limit of 3 hours. Developed by Liang Wen, Fenrui Xiao, Xin He, Yunke Cai, Qi An, Zhenyu Duan, Yimin Du, Junchen Liu, Lifu Tang, Xiaowei Lv, Haosheng Zou, Yongchao Deng, Shousheng Jia and Xiangzheng Zhang, the model surpasses previous open-source alternatives on competitive math benchmarks. Incredibly, the researchers completed the model’s training in fewer than six hours on 12 Nvidia H800 GPUs at an estimated total cost of $1,000. This makes Light-R1-32B one of the most accessible and practical approaches for developing high-performing math-specialized AI models. However, it’s important to remember that the model was trained on a variant of Alibaba’s open-source Qwen 2.5-32B-Instruct, which itself is presumed to have had much higher upfront training costs. Alongside the model, the team has released its training datasets and scripts and evaluation tools, providing a transparent and accessible framework for building math-focused AI models. The arrival of Light-R1-32B follows similar efforts from rivals, such as Microsoft Orca-Math. A new math king emerges To help Light-R1-32B tackle complex mathematical reasoning, the researchers trained on a model that wasn’t equipped with long-chain-of-thought (COT) reasoning. They applied curriculum-based supervised fine-tuning (SFT) and direct preference otptimization (DPO) to refine its problem-solving capabilities. When evaluated, Light-R1-32B achieved 76.6 on AIME24 and 64.6 on AIME25, surpassing DeepSeek-R1-Distill-Qwen-32B, which scored 72.6 and 54.9, respectively. This improvement suggests that the curriculum-based training approach effectively enhances mathematical reasoning, even when training from models that initially lack long COT. Fair benchmarking To ensure fair benchmarking, the researchers decontaminated training data against common reasoning benchmarks, including AIME24/25, MATH-500 and GPQA Diamond, preventing data leakage. They also implemented difficulty-based response filtering using DeepScaleR-1.5B-preview, ultimately forming a 76,000-example dataset for the first stage of supervised fine-tuning. A second, more challenging dataset of 3,000 examples further improved performance. After training, the team merged multiple trained versions of Light-R1-32B, leading to additional gains. Notably, the model maintains strong generalization abilities on scientific reasoning tasks (GPQA), despite being math-specialized. How enterprises can benefit Light-R1-32B is released under the Apache License 2.0, a permissive open-source license that allows free use, modification and commercial deployment without requiring derivative works to be open-sourced. This makes it an attractive option for enterprises, AI developers and software engineers looking to integrate or customize the model for proprietary applications. The license also includes a royalty-free, worldwide patent grant, reducing legal risks for businesses while discouraging patent disputes. Companies can freely deploy Light-R1-32B in commercial products, maintaining full control over their innovations while benefiting from an open and transparent AI ecosystem. For CEOs, CTOs and IT leaders, Apache 2.0 ensures cost efficiency and vendor independence, eliminating licensing fees and restrictive dependencies on proprietary AI solutions. AI developers and engineers gain the flexibility to fine-tune, integrate and extend the model without limitations, making it ideal for specialized math reasoning, research and enterprise AI applications. However, as the license provides no warranty or liability coverage, organizations should conduct their own security, compliance and performance assessments before deploying Light-R1-32B in critical environments. Transparency in low-cost training and optimization for math problem solving The researchers emphasize that Light-R1-32B provides a validated, cost-effective way to train strong long CoT models in specialized domains. By sharing their methodology, training data and code, they aim to lower cost barriers for high-performance AI development. Looking ahead, they plan to explore reinforcement learning (RL) to further enhance the model’s reasoning capabilities. source

NVIDIA GeForce RTX 5070. Image: NVIDIA CEO Jensen Huang made a bold claim about NVIDIA’s new GeForce RTX 5070 budget graphics card at CES 2025 that stirred both excitement and skepticism, creating a lightning rod for controversy. Huang hyped the card as a revolutionary breakthrough, touting its ability to deliver next-generation performance at a surprisingly low price of $550 — claims early technical reviews have called into question. Despite Huang’s claims at CES 2025, benchmark tests show the RTX 5070 barely outpaces last year’s 4070 Super without the heavy assist of DLSS interpolation. Notable tech channels like Gamers Nexus and Linus Tech Tips have criticized NVIDIA for overstating the card’s capabilities, with some reviewers accusing the company of misleading consumers by comparing apples to oranges. Still, the RTX 5070 is selling out almost immediately, with scalpers demanding nearly double the market price. What’s hot at TechRepublic Underwhelming performance, market mayhem Digging deeper into the card’s performance shows that NVIDIA’s innovative DLSS Multi-Frame Generation is a double-edged sword. While it can artificially inflate frame rates by generating additional frames, this technique comes at a steep cost: sluggish gameplay and noticeable visual artifacts that undermine the user experience in demanding titles. As a result, the RTX 5070 struggles to deliver a genuine upgrade over its predecessor despite the hype. Meanwhile, the graphics card market’s chronic undersupply has pushed prices well above the official MSRP. With stocks selling out almost instantly, scalpers have taken advantage by listing the card for upward of $1,000, further complicating an already murky picture. SEE: AI Surge Could Trigger Global Chip Shortage by 2026 Competitive sparks amid scarcity Amid NVIDIA’s overpromised spectacle, AMD’s Radeon RX 9070 series has emerged as a compelling, if not flawless, alternative. Early reviews have been kinder to AMD’s approach, noting that while the RX 9070 also faces pricing pressures, it delivers performance that more accurately reflects its capabilities. This head-to-head contrast between NVIDIA’s smoke-and-mirrors marketing and AMD’s straightforward presentation underscores the broader instability gripping the graphics card market, where scarcity and speculative pricing continue to drive consumer behavior. What’s next for RTX 5070? As gamers and tech enthusiasts await fresh stock and clearer performance data, the RTX 5070 saga serves as a cautionary tale about the perils of overpromising in the tech landscape. For now, NVIDIA’s budget card — despite its shortcomings — remains a hot commodity, proving that even smoke and mirrors can sometimes ignite unexpected demand. This article was written by freelance writer Sunny Yadav. source

By Bryan Koenig ( March 12, 2025, 5:43 PM EDT) — The U.S. Department of Justice doubled down on its arguments against permitting Apple to intervene in the upcoming remedies phase of its Google search monopoly lawsuit, arguing that the newly submitted final version of its sought fixes show Apple would keep getting payments it wants protected…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Cerebras Systems, an AI hardware startup that has been steadily challenging Nvidia’s dominance in the artificial intelligence market, announced Tuesday a significant expansion of its data center footprint and two major enterprise partnerships that position the company to become the leading provider of high-speed AI inference services. The company will add six new AI data centers across North America and Europe, increasing its inference capacity twentyfold to over 40 million tokens per second. The expansion includes facilities in Dallas, Minneapolis, Oklahoma City, Montreal, New York and France, with 85% of the total capacity located in the United States. “This year, our goal is to truly satisfy all the demand and all the new demand we expect will come online as a result of new models like Llama 4 and new DeepSeek models,” said James Wang, director of product marketing at Cerebras, in an interview with VentureBeat. “This is our huge growth initiative this year to satisfy [the] almost unlimited demand we’re seeing across the board for inference tokens.” The data center expansion represents the company’s ambitious bet that the market for high-speed AI inference — the process where trained AI models generate outputs for real-world applications — will grow dramatically as companies seek faster alternatives to GPU-based solutions from Nvidia. Cerebras plans to expand capacity from 2 million to over 40 million tokens per second by Q4 2025 across eight data centers in North America and Europe. (Credit: Cerebras) Strategic partnerships that bring high-speed AI to developers and financial analysts Alongside the infrastructure expansion, Cerebras announced partnerships with Hugging Face, the popular AI developer platform, and AlphaSense, a market intelligence platform widely used in the financial services industry. The Hugging Face integration will allow its five million developers to access Cerebras Inference with a single click, without having to sign up for Cerebras separately. This becomes a major distribution channel for Cerebras, particularly for developers working with open-source models like Llama 3.3 70B. “Hugging Face is kind of the GitHub of AI and the center of all open-source AI development,” Wang explained. “The integration is super nice and native. [We] just appear in their inference providers list. You just check the box and then you can use Cerebras right away.” The AlphaSense partnership represents a significant enterprise customer win, with the financial intelligence platform switching from what Wang described as a “global, top-three closed-source AI model vendor” to Cerebras. AlphaSense, which serves approximately 85% of Fortune 100 companies, is using Cerebras to accelerate its AI-powered search capabilities for market intelligence. “This is a tremendous customer win and a very large contract for us,” Wang said. “We speed them up by 10 times, so what used to take five seconds or longer basically become[s] instant on Cerebras.” Mistral’s Le Chat, powered by Cerebras, processes 1,100 tokens per second — significantly outpacing competitors like Google’s Gemini, ChatGPT and Claude. (Credit: Cerebras) How Cerebras is winning the race for AI inference speed as reasoning models slow down Cerebras has been positioning itself as a specialist in high-speed inference, claiming its Wafer-Scale Engine (WSE-3) processor can run AI models 10 to 70 times faster than GPU-based solutions. This speed advantage has become increasingly valuable as AI models evolve toward more complex reasoning capabilities. “If you listen to Jensen’s remarks, reasoning is the next big thing, even according to Nvidia,” Wang said, referring to Nvidia CEO Jensen Huang. “But what he’s not telling you is that reasoning makes the whole thing run 10 times slower because the model has to think and generate a bunch of internal monologue before it gives you the final answer.” This slowdown creates an opportunity for Cerebras, whose specialized hardware is designed to accelerate these more complex AI workloads. The company has already secured high-profile customers including Perplexity AI and Mistral AI, who use Cerebras to power their AI search and assistant products, respectively. “We help Perplexity become the world’s fastest AI search engine. This just isn’t possible otherwise,” Wang said. “We help Mistral achieve the same feat [with AI assistants]. Now they have a reason for people to subscribe to Le Chat Pro, whereas before, your model is probably not the same cutting-edge level as GPT-4.” Cerebras’ hardware delivers inference speeds up to 13x faster than GPU solutions across popular AI models like Llama 3.3 70B and DeepSeek-R1 70B. (Credit: Cerebras) The compelling economics behind Cerebras’ challenge to OpenAI and Nvidia Cerebras is betting that the combination of speed and cost will make its inference services attractive even to companies already using leading models like GPT-4. Wang pointed out that Meta’s Llama 3.3 70B, an open-source model that Cerebras has optimized for its hardware, now scores the same on intelligence tests as OpenAI’s GPT-4, while costing significantly less to run. “Anyone who is using GPT-4 today can just move to Llama 3.3 70B as a drop-in replacement,” he explained. “The price for GPT-4 is [about] $4.40 in blended terms. And Llama 3.3 is like 60 cents. We’re about 60 cents, right? So you reduce cost by almost an order of magnitude. And if you use Cerebras, you increase speed by another order of magnitude.” Inside Cerebras’ tornado-proof data centers built for AI resilience The company is making substantial investments in resilient infrastructure as part of its expansion. Its Oklahoma City facility, scheduled to come online in June 2025, is designed to withstand extreme weather events. “Oklahoma, as you know, is a kind of a tornado zone. So this data center actually is rated and designed to be fully resistant to tornadoes and seismic activity,” Wang said. “It will withstand the strongest tornado ever recorded on record. If [such a tornado] goes through, this thing will just keep sending Llama tokens to developers.” The Oklahoma City facility, operated in partnership with Scale Datacenter, will house over 300 Cerebras CS-3 systems and feature triple-redundant power stations and custom water-cooling solutions specifically

There are several advantages to using Kubernetes: it ensures a high degree of flexibility when it comes to selecting the right cloud for the respective application. And it increases the availability and reliability of services. For example, Kubernetes can automatically redirect workloads to other providers if a provider fails or the connection is poor — or to make optimal use of flat-rate data volumes. Terraform Terraform, an open-source tool for Infrastructure as Code (IaC), is recommended for building an infrastructure for application environments. It allows you to define and manage resources such as virtual machines, networks and databases using declarative configuration files. Instead of manually creating and managing infrastructure resources, the IT or cloud architects merely describe the desired end state of their infrastructure and save it as configuration files. The configuration language HashiCorp Configuration Language (HCL) is used for the description. Terraform then independently generates the desired state by creating, modifying or deleting the necessary resources. The whole thing can be set up as often as you like. A short command is all it takes to automatically copy an environment once it has been created. This is useful, for example, when setting up staging environments that are needed at different stages in the software development process. It is useful, for example, when developing cloud applications in highly regulated industries such as banking and insurance, aerospace, utilities and automotive. source

By Matthew Perlman ( March 11, 2025, 5:06 PM EDT) — An official for the Competition and Markets Authority said the agency will focus enforcement efforts against technology companies on issues that have a local impact in the United Kingdom and is less likely to act on issues already being addressed by other authorities…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

Deneen DeFiore, VP and CISO, United Airlines Deneen DeFiore / United Airlines DeFiore and CIO Jason Birnbaum got a head start on their relationship dynamics working at General Electric, where they didn’t interact as colleagues, but still gained exposure to a shared set of experiences, core values, and business language. That mutual understanding was pivotal when it came time to sketch out the contours of their working partnership at United. DeFiore and Birnbaum built on their common foundation, prioritizing open communications and transparency, developing a shared vision and set of outcomes, and aligning messaging to help break down barriers and misperceptions. Their playbook helps position security requirements at the center of new initiatives without bogging down timelines or becoming a gating factor for innovation. Case in point: United’s “Every Flight Has a Story” offering, a generative AI-fueled flight-status service released last year designed to bring more transparency and context to flight delays and updates. Jason Birnbaum, CIO, United Airlines Jason Birnbaum / United Airlines Working as a team, DeFiore and Birnbaum recognized the game-changing potential for generative AI, and together with their organizations created a framework around responsible use of the technology. The flight-status service was one of the first external-facing use cases for gen AI, and there are about 90 others in the pipeline, she says. “We were able to iterate on that quickly together and manage the risks associated with using emerging technology,” she explains. source

In a landmark move for Saudi Arabia’s digital economy, Huawei Cloud and Zain KSA have announced a strategic partnership to accelerate cloud adoption and digital transformation across industries. Signed at MWC Barcelona 2025, this collaboration aligns with Saudi Arabia’s Cloud-First policy and underscores both companies’ commitment to advancing cloud technologies in the Kingdom. Huawei showcased cutting-edge advancements at MWC, reaffirming its 5G, AI, and cloud computing leadership. The company introduced next-generation cloud solutions for scalability, security, high-performance computing (HPC), and AI-driven services that optimize business operations. Huawei Cloud’s latest offerings highlight its focus on sovereign cloud solutions, ensuring data compliance and security while delivering seamless AI-powered services. These innovations set the stage for the newly announced partnership with Zain KSA, reinforcing the Kingdom’s ambitions for digital transformation. Under the agreement, Huawei Cloud will provide advanced cloud services, AI-powered tools, and comprehensive technical support. At the same time, Zain KSA will play a pivotal role in delivering these solutions to businesses. This ensures that organizations in Saudi Arabia can seamlessly integrate cloud computing into their digital transformation strategies, fostering a more competitive and innovative business environment. source