The Dell Laptop Buying Guide UK: Which Dell Laptop Should You Buy? When you purchase through links in our articles, we may earn a small commission. Microsoft's Surface Laptop Ultra is part productivity machine, part AI workstation. But can you afford it? Microsoft's Surface Laptop Ultra is a new class of device, combining efficient productivity, powerful graphics, plus AI capabilities, all for a price that seems totally unrealistic in the 2026 economy. Microsoft's Surface Laptop Ultra and its newfangled Nvidia RTX Spark chip characterize what many would call a new axis of productivity: AI. With it comes an old, familiar feeling of FOMO, or the fear of missing out. For decades, the equation has been simple: A faster CPU means applications run faster. A faster GPU equates to higher frame rates in PC games. Now there's AI, which means memory matters. Memory is shared between the Arm CPU and powerful RTX GPU inside the Nvidia Spark N1X chip powering the Surface Laptop Ultra, and more memory means the ability to load more powerful AI models. As regards the Surface Laptop Ultra model that Microsoft shipped us, my first impression was one of disappointment, as it came with just a "measly" 32GB of memory (along with its whopping $3,699 price tag).

When you purchase through links in our articles, we may earn a small commission. Shortages are driving prices up. Here's how to stretch your current rig until the market calms down. There's no denying that all of our next PC upgrades are likely to be more expensive than they've been in a long time. And if history is anything to go by, they won't come all the way down again.

Gear Computers Apple's new M6 Mac mini and M5 Ultra Mac Studio: 10 details you might have missed The new Apple silicon desktops boast 2nm chips, quad-die architecture, and massive AI multipliers. Here is the fine print before you upgrade. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. We may earn revenue from the products available on this page and participate in affiliate programs. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

A.1 Dataset Examples450 In this section of the appendix, we present a detailed overview of several representative tasks from451 each category included in REASONINGGYM. For each task, we describe its structure, complexity452 parameters, and provide examples.453 A.1.1 complex_arithmetic(Algebra)454 Find the solution of an arithmetic operation involving complex numbers.455 The spiral order is clockwise, starting from the top-left corner. Predict the corresponding output grid by applying the rule you found.

Large language model inference with long contexts often produces key-value (KV) caches whose footprint exceeds the capacity of high bandwidth memory on a GPU. Prior LLM inference frameworks such as vLLM mitigate this pressure by swapping KV cache pages to host DRAM. However, the high cost of large DRAM pools makes this solution economically unattractive. Although offloading to SSDs can be a cost-effective way to expand memory capacity relative to DRAM, conventional frameworks such as FlexGen experience a substantial throughput drop since the data path that routes SSD traffic through CPU to GPU is severely bandwidth-constrained. To overcome these limitations, we introduce HiFC, a novel DRAM free swapping scheme that enables direct access to SSD-resident memory with low latency and high effective bandwidth. HiFC stores KV pages in pseudo-SLC (pSLC) regions of commodity NVMe SSDs, sustaining high throughput under sequential I/O and improving write endurance by up to 8$\times$. Leveraging GPU Direct Storage, HiFC enables direct transfers between SSD and GPU, bypassing host DRAM and alleviating PCIe bottlenecks. HiFC employs fine-grained block mapping to confine writes to high-performance pSLC zones, stabilizing latency and throughput under load. HiFC achieves inference throughput comparable to DRAM-based swapping under diverse long-context workloads, such as NarrativeQA, while significantly lowering the memory expansion cost of a GPU server system by 4.5$\times$ over three years.

Due to the explosive growth of artificial intelligence, it is estimated that data centers will consume up to 12 percent of total U.S. electricity by 2028, according to the Lawrence Berkeley National Laboratory. Improving data center energy efficiency is one way scientists are striving to make AI more sustainable. Toward that goal, researchers from MIT and the MIT-IBM Watson AI Lab developed a rapid prediction tool that tells data center operators how much power will be consumed by running a particular AI workload on a certain processor or AI accelerator chip. Their method produces reliable power estimates in a few seconds, unlike traditional modeling techniques that can take hours or even days to yield results. Moreover, their prediction tool can be applied to a wide range of hardware configurations -- even emerging designs that haven't been deployed yet.

Kernel matrix-vector multiplication (KMVM) is one of the most important operations needed in scientific computing with core applications indiffeomorphic registration, geometric learning [11], [31],numerical analysis [28],fluid dynamics [6],and machine learning [27].

In PU learning, a binary classifier is trained from positive (P) and unlabeled (U) data without negative (N) data. Although N data is missing, it sometimes outperforms PN learning (i.e., ordinary supervised learning). Hitherto, neither theoretical nor experimental analysis has been given to explain this phenomenon. In this paper, we theoretically compare PU (and NU) learning against PN learning based on the upper bounds on estimation errors. We find simple conditions when PU and NU learning are likely to outperform PN learning, and we prove that, in terms of the upper bounds, either PU or NU learning (depending on the class-prior probability and the sizes of P and N data) given infinite U data will improve on PN learning. Our theoretical findings well agree with the experimental results on artificial and benchmark data even when the experimental setup does not match the theoretical assumptions exactly.

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