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The post NVIDIA Pushes Hardware-Aware LLM Co-Design for AI Efficiency appeared on BitcoinEthereumNews.com. Rongchai Wang Jul 10, 2026 17:13 NVIDIA highlights hardware-friendly AI model design to optimize LLM performance. Learn how co-design boosts throughput, latency, and cost-efficiency. NVIDIA has detailed its approach to hardware-aware large language model (LLM) design, a strategy that simultaneously optimizes AI model architectures and the hardware they run on. This co-design approach aims to maximize throughput, reduce latency, and lower costs for LLM deployments across data centers and edge devices. The blog post, published on July 10, 2026, provides practical guidelines for aligning AI models with modern GPU capabilities. Co-design focuses on balancing three core metrics for AI performance: accuracy, throughput, and interactivity. For instance, NVIDIA stresses that small design choices, such as aligning model dimensions with GPU tile sizes or choosing wider mod
Read full articleMeta's AI policy reversal highlights the critical need for transparent consent mechanisms, impacting trust and future AI feature rollouts. The post Meta reverses course on AI use for public Instagram profiles appeared first on Crypto Briefing.
Meta's AI advancements could strain global AI hardware supply, impacting costs and availability for smaller tech and crypto-AI ventures. The post Meta stock surges 15% in best weekly rally since early 2024, fueled by AI momentum appeared first on Crypto Briefing.
The city of San Jose, Calif.’s training course has enabled employees — including those with little to no experience using AI technologies — to develop their own AI-powered tools.
The post NVIDIA Optimizes JAX LLM Training with Host Offloading appeared on BitcoinEthereumNews.com. Lawrence Jengar Jul 10, 2026 18:51 NVIDIA’s host offloading for JAX LLM training boosts GPU memory efficiency, enabling larger batch sizes and faster throughput. NVIDIA has introduced a new host offloading technique for JAX-based large language model (LLM) training, addressing GPU high-bandwidth memory (HBM) bottlenecks that often limit the scalability of modern AI workloads. Leveraging the latest NVIDIA Blackwell architecture, this approach enables larger batch sizes and faster training throughput by moving selected activations to CPU memory during the forward pass and streaming them back during the backward pass. HBM is frequently a limiting factor in LLM training as model sizes, sequence lengths, and batch sizes grow. NVIDIA’s host offloading solution, detailed in a company blog post published on July 10, 2026, offers an alternative to activation rematerialization,
The post China is giving Manus to Tencent after blocking Meta’s attempted acquisition appeared on BitcoinEthereumNews.com. China is pushing Manus toward a new group of owners after telling Meta Platforms (NASDAQ: META) to reverse its $2 billion purchase of the AI agent startup. As per the Financial Times, Tencent Holdings (HKEX: 0700) will hold the maximum stake, but not more than 50 percent. It means that Manus will remain an independent entity and will continue its operations from Singapore instead of becoming a part of Tencent. The new deal would keep the same $2 billion valuation used when Meta bought Manus in December 2025. Former investors such as Tencent, ZhenFund, and HSG are taking part in the talks, along with Manus executives. Other investors could still join before an agreement is reached. China forces investors to rebuild Manus after Meta separates the business Beijing ordered Meta to reverse the purchase in April 2026, saying the deal broke local investment rules. Manus c
The US policy shift strengthens UAE tech ties, boosting AI and crypto sectors, but geopolitical risks could reverse these gains if trust falters. The post US eases export controls on advanced chips and drones for UAE in major policy shift appeared first on Crypto Briefing.
SK Hynix's NASDAQ debut highlights the growing importance of AI infrastructure, potentially reshaping global investment dynamics in tech. The post SK Hynix makes historic NASDAQ debut amid AI-driven demand appeared first on Crypto Briefing.
The post NVIDIA CUDA Kernel Fusion Boosts GPU Efficiency in AI Workloads appeared on BitcoinEthereumNews.com. Timothy Morano Jul 10, 2026 17:21 NVIDIA’s CUDA kernel fusion cuts memory traffic, kernel launch overhead, and speeds up AI and HPC tasks by up to 3x. Key for MoE and LLM training. NVIDIA (NASDAQ: NVDA) is doubling down on GPU efficiency with its latest advancements in CUDA kernel fusion, a technique that optimizes memory usage and minimizes kernel launch overhead. By combining multiple operations into a single kernel, NVIDIA claims speedups of up to 3x for certain workloads like Mixture-of-Experts (MoE) models and large language model (LLM) training. Kernel fusion works by addressing a longstanding bottleneck in GPU computing: the high memory bandwidth consumption caused by intermediate data transfers. In a typical GPU workload, intermediate results often travel through global memory between separate kernel launches, creating significant latency. Fusion elimi
