Latest news with #Quali
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First Post
29-06-2025
- Automotive
- First Post
'Game over, Carlos, jump out': Sainz's Williams catches fire before start of Austrian Grand Prix
Carlos Sainz's Williams car caught fire before the start of the 2025 Austrian Grand Prix, forcing him to retire early. Sainz is being trolled online as this is the second time his car caught fire in Austrian GP in two years. Meanwhile, Max Verstappen also crashed out after a collision with Kimi Antonelli on Lap 1. read more Carlos Sainz is stuck at the end of the pit after his car broke down. Image: Reuters Spanish F1 driver Carlos Sainz had a nightmare at the Austrian Grand Prix on Sunday after his Williams car caught fire before the race even started. The incident forced a 10-minute delay to the start and ended Sainz's day before he could even compete. Sainz, who was starting from 19th on the grid, reported brake issues just before smoke started pouring out from the rear of his car. Moments later, flames were visible. Luckily, he was close to the pit entrance and marshals rushed over quickly to put out the fire. STORY CONTINUES BELOW THIS AD 'Game over, Carlos, jump out,' Sainz's race engineer Gaetan Jego said on the team radio after the car caught fire. 'There's damage in the car, for sure,' Sainz reported. 'The car is undriveable. When I say undriveable, it's pulling under braking, no load in high-speed. Undriveable,' Sainz said after retiring from the race. 'We've just seen that we had quite a bit of damage on the floor, so we were lacking quite a lot of downforce. We also had an issue with the brakes from the start of Quali, so too many things going to on to actually push around a high-confidence track like this,' he added. Fans online were quick to react to the drama, with many poking fun at Sainz's misfortune. Coincidentally, Sainz's car had caught fire at the Austrian GP last year as well. He was driving for Ferrari at the time. Things have gone from bad to worse for Carlos 😩 He won't start the race ❌#F1 #AustrianGP — Formula 1 (@F1) June 29, 2025 Verstappen restires after crash with Antonelli Max Verstappen suffered his first retirement of the Formula 1 season on Sunday after a collision with Mercedes' Kimi Antonelli on the opening lap of Red Bull's home Austrian Grand Prix. The four-times world champion had started seventh on the grid, with Italian rookie Antonelli ninth, at a circuit where he has won a record five times. Antonelli, who clearly caused the Turn Three collision, also retired and the safety car was deployed. STORY CONTINUES BELOW THIS AD 'I'm out, I got hit like crazy,' Verstappen, third in the championship going into the race, said over the team radio. 'Sorry about that, I locked the rear,' Antonelli told his team. The retirement ended a run of 31 grands prix in the points for the Dutch driver, whose fans throng in their thousands to the Red Bull Ring. (With agency inputs)
Yahoo
05-04-2025
- Automotive
- Yahoo
Grass fires again disrupt Japanese Grand Prix practice as Lando Norris sets pace
Final practice at the Japanese Grand Prix was again disrupted by grass fires at the side of the track as Lando Norris set the pace. Friday's second session was halted twice due to the issue, with dry grass catching fire from the sparks thrown up by the skid blocks under the cars. The FIA said on Friday evening that preventative measures, including dampening the grass and stationing specific response teams around the track, had been taken. But it took just six minutes for Saturday's running in the Suzuka sunshine to be brought to a halt as marshals were sent out with fire extinguishers to deal with more flames. Another grass fire causes a red flag in FP3 😳👇 — Sky Sports F1 (@SkySportsF1) April 5, 2025 The problem returned with six minutes of the hour-long session remaining as the red flag was brought out for a second time. With warm weather forecast for the rest of Saturday, it could prove to be an issue which affects qualifying later in the afternoon. McLaren have won both races so far this season – one each for Norris and Oscar Piastri – and both topped a practice session on Friday. The pair duelled it out again for the fastest time and it was Norris who topped the standings, just 0.026 seconds ahead of the Australian. Another disrupted practice session at Suzuka. Next up, Quali 🔜 — Mercedes-AMG PETRONAS F1 Team (@MercedesAMGF1) April 5, 2025 The Championship leader did not have it all his own way. Norris had a few struggles on Friday before topping the standings in the opening running. He was again off the track at Degner two on Saturday before putting in his fastest lap. Mercedes' George Russell again showed signs that he could be the man to take the fight to the McLaren duo as he finished third fastest, just over a tenth off the pace. Ferrari's Charles Leclerc was the fourth fastest but over four tenths off Norris' time, with Max Verstappen fifth ahead of Lewis Hamilton. A sizeable crash for Jack Doohan in FP2 😱 He is OK and out of the car#F1 #JapaneseGP — Formula 1 (@F1) April 4, 2025 Alpine's Jack Doohan took part in the session after his heavy crash at the start of FP2 on Friday. The team confirmed all parts on the car had been replaced other than the power unit, after the high-speed crash which they said was caused by a failure to close the DRS heading into turn one. Qualifying for Sunday's race begins at 1500 local time (0700 BST).
Yahoo
18-03-2025
- Business
- Yahoo
Quali Streamlines Delivery of Agentic AI at Scale with NVIDIA AI Enterprise
New integrations lower the barrier to adoption for Agentic AI by automating management across development resources, including Large Language Models (LLMs), data services, GPU infrastructure, and more to deliver high-performing AI agents. AUSTIN, Texas, March 18, 2025 (GLOBE NEWSWIRE) -- Quali, a leading provider of platform engineering solutions for infrastructure automation and management, today announced its integration with NVIDIA AI Enterprise software, including NVIDIA NIM™ microservices and NVIDIA AI Blueprints, to simplify the creation and management of Agentic AI solutions. Agentic AI represents a transformational opportunity for today's enterprises, with implications across internal operations and customer experience. By leveraging NVIDIA NIM microservices and NVIDIA AI Blueprints included in the NVIDIA AI Enterprise software platform, the Quali Torque Software-as-a-Service platform simplifies the orchestration and management of each layer of the Agentic AI tech stack: accelerated infrastructure, cloud services and data pipelines, LLMs and AI models, and AI agents and applications. With Torque providing unified orchestration, lifecycle management, and cost optimization, each layer of the development stack seamlessly supports each other to ensure reliability, accuracy, and efficiency. To accomplish this, Torque manages the entire infrastructure lifecycle using Environments as Code (EaC)—a model that transforms cloud resources into fully managed, self-service environments supporting mission-critical operations, such as AI workloads, software development, demos, training, and more. Quali can deploy and manage the entire tech stack supporting Agentic AI solutions as a stateful, dynamic environment through the integration of NVIDIA technology. This approach cuts through complexity and automates routine tasks, enabling more organizations to adopt Agentic AI faster, scale more efficiently, and focus on innovation rather than infrastructure management. Key highlights of this release include: Easy-to-Use Modules of NVIDIA AI Enterprise & Other NVIDIA Resources: Torque creates reusable modules defining each component needed to deliver an AI agent, including NVIDIA accelerated compute, NVIDIA NIM microservices, and pre-trained models and data science frameworks included with NVIDIA AI Enterprise. This normalization enables no-code orchestration of each layer of the stack supporting the AI solution, thereby accelerating delivery and lowering the barrier to adoption of Agentic AI. AI-Driven Environment Design, Creation, & Reusability: Torque's AI Copilot leverages these reusable modules to design and generate new environment blueprints in response to user-submitted prompts. Additionally, Torque's graphical environment design tool allows users to drag-and-drop resources and set dependencies visually. As the user adjusts the design of the environment, Torque modifies the code in the environment blueprint file automatically, further reducing the need for complex coding to support the AI solution. Once complete, Torque saves this blueprint as a reusable file that can be deployed, maintained, and monitored continuously. Simplified Provisioning & Maintenance of Individual Layers of the Agentic AI Tech Stack: Torque executes the code to provision each layer of the AI tech stack, monitors the state of those resources continuously, and notifies users about anomalies, including infrastructure errors, configuration drift, and other unexpected updates. This allows Torque administrators to tailor the user experience to the people responsible for delivering each layer of the AI tech stack, enabling them to reconcile errors and other unexpected issues proactively. Streamlined Integration of Each Layer of the AI Tech Stack: Once provisioned as a managed environment in Torque, each layer of the AI tech stack can be published so other users can access it as an input supporting other layers of the stack. For example, a developer building an AI agent can select the live GPU clusters, cloud-based data services, and AI models they need from a simple pick list made available in Torque's provisioning experience. Torque leverages those inputs to provision the AI agent, while also enabling users to maintain the live environment supporting each layer of the stack. This provides a seamless experience that cuts through complexity and accelerates the delivery of full-stack AI agents. Automating Critical Tasks for AI Performance: Torque workflows can define and automatically execute routine tasks required to maintain high-performing AI solutions, such as training and data quality assurance. This eliminates manual work required for day-to-day maintenance, while also providing visibility for users to understand when these tasks were last executed, how effective they were, and other relevant information. Dynamic GPU Scaling in Response to Application Needs: As the various AI models supporting the agent transition through mission-critical phases, Torque automatically scales GPUs up and down. This provides adequate computing capacity to support even the most resource-intensive workloads like training, while scaling down capacity to prevent costly over-provisioning for less resource-intensive tasks like inference. 'Complexity has always been at the core of the problems we solve for our customers and partners,' said Lior Koriat, Quali CEO. 'As more organizations look to embrace AI, the ability to cut through complexity is the key to delivering the kinds of AI experiences that customers expect. We're thrilled to develop a streamlined approach for delivering impactful AI solutions leveraging NVIDIA AI, and we look forward to helping our community unlock these opportunities.' About Quali: Headquartered in Austin, Texas, Quali provides platform engineering tools to help enterprise technology and engineering teams accelerate and optimize the use of multi-cloud infrastructure. Global 2000 enterprises rely on Quali's solutions to democratize cloud access securely and efficiently by simplifying the experience of deploying application environments and enforcing cloud governance at scale. For more information, please visit and follow Quali on LinkedIn. Contact Information: Colin Neagle VP of Marketing Quali Colin.n@
