The Dell Laptop Buying Guide 2026: Which XPS or Alienware Laptop Should You Buy? 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. Nvidia's RTX Spark laptops make no sense -- until they do We've tested Nvidia's wild new chips for AI, gaming, and everyday work. Nvidia makes laptop CPUs now.

Lawyers say clients who use chatbots are adding to their workload, forcing them to correct errors made by AI. Lawyers say clients who use chatbots are adding to their workload, forcing them to correct errors made by AI. Research finds 55% of lawyers are using AI in legal practices, up from 37% last year, though efficiency gains aren't as high as expected More than half of Victoria's lawyers have reported using AI in their work, according to a new study, but just over a quarter say they have reduced fees as a result of efficiency gains. They also say clients seeking advice from chatbots have added to their workload, forcing them to correct errors made by AI. The 2026 census of 2,221 of the state's lawyers, conducted by the Victorian Legal Services Board and Commissioner's legal services research centre, found a big increase in lawyers using AI in their practices, rising from 37% to 55% in the last year.

The Dell Laptop Buying Guide 2026: Which XPS or Alienware Laptop Should You Buy? 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. My absurd AI workload found them. Nvidia's RTX Spark isn't going to replace a GeForce RTX 5090 for most workloads, but give it a massive AI model and suddenly its 128GB of unified memory becomes a superpower. Asus' $7,000 ProArt P16 chewed through my enormous local AI research workloads and even finished one nearly 20 minutes faster than my monster RTX 5090 desktop. It's wildly expensive, the fans scream under sustained AI loads, and battery life gets absolutely demolished.

As AI moves from pilots to production, enterprises must decide when consumption pricing still fits--and when AI infrastructure should be treated as a productive asset. When customers talk about AI costs, the conversation usually starts with token prices and ends with access to the latest, most capable model in the cloud. Do they always need that level of capability? But that is often where the conversation goes. As AI moves from experimentation to production, model choice is only part of the equation. When demand becomes steady and business-critical, a consumption-only approach can turn AI spending into a variable monthly line item that is difficult to forecast as usage, workloads, and model requirements change.

With AI inference now driving enterprise workloads, organizations must rethink infrastructure for speed, efficiency, scalability, and performance per watt to unlock AI's real-world potential. The era of AI inference has arrived. Imagine a healthcare system analyzing millions of data points in real time to accelerate life-saving medical research, or an intelligent assistant instantly resolving thousands of complex customer needs at once. These real-world breakthroughs rely on advanced infrastructure acting as the engine of continuous intelligence, powering real-time services while also supporting an increasingly intelligent edge of IoT and consumer devices. However, in this inference-driven landscape, every delay, bottleneck, or wasted watt directly affects human outcomes and operating costs. This shift changes what infrastructure must deliver.

Look Up Say More Versus Creator Hub Switch Off Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Trending Now Safety Net In My Bag VidCon with Mashable Back to School Furtastic All Series All of Apple's laptops are excellent. But if you're not sure which model is right for you, I can offer some buying guidance. Haley Henschel is a Chicago-based Senior Shopping Reporter at Mashable who reviews and finds deals on popular tech, from laptops to gaming consoles and VPNs. She has years of experience covering shopping holidays and can tell you what's actually worth buying on Black Friday and Amazon Prime Day. Her work has also explored the driving forces behind digital trends within the shopping sphere, from dupes to 12-foot skeletons . All products featured here are independently selected by our editors and writers. If you buy something through links on our site, Mashable may earn an affiliate commission. MacBooks have been ridiculously easy to recommend since we first started reviewing them in the mid-2010s, but since Apple rolled out its first M-series chips in 2020.

When you purchase through links in our articles, we may earn a small commission. Run private, reliable local AI workflows with MSI's Cubi NUC AI+ 3MG mini PC, built for compact on-device AI, agents, and secure automation. Agentic AI is the current buzzword of the day, but if you're chatting with ChatGPT, having Gemini make images for you, or coding with Claude, then you're almost exclusively interacting with a cloud AI ecosystem. This is great for the most capable, frontier models, but it's dependent on server uptime, gives few guarantees for privacy and security, and once you start running agents, token costs can quickly run away from you. For smaller, everyday AI tasks, persistent agentic work, or partnering with more powerful cloud or local AI tools, small-scale local AI PCs can be ideal.

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 pseudoSLC (pSLC) regions of commodity NVMe SSDs, sustaining high throughput under sequential I/O and improving write endurance by up to 8 . 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 DRAMbased swapping under diverse long-context workloads, such as NarrativeQA, while significantly lowering the memory expansion cost of a GPU server system by 4.5 over three years.

Speculative decoding is widely adopted to reduce latency in large language model (LLM) inference by leveraging smaller draft models capable of handling diverse user tasks. However, emerging AI applications, such as LLM-based agents, present unique workload characteristics: instead of diverse independent requests, agentic frameworks typically submit repetitive inference requests, such as multi-agent pipelines performing similar subtasks or self-refinement loops iteratively enhancing outputs. These workloads result in long and highly predictable sequences, which current speculative decoding methods do not effectively exploit. To address this gap, we introduce SuffixDecoding, a novel method that utilizes efficient suffix trees to cache long token sequences from prompts and previous outputs. By adaptively speculating more tokens when acceptance likelihood is high and fewer when it is low, SuffixDecoding effectively exploits opportunities for longer speculations while conserving computation when those opportunities are limited. Evaluations on agentic benchmarks, including SWE-Bench and Text-to-SQL, demonstrate that SuffixDecoding achieves speedups of up to 5.3, outperforming state-of-the-art methods--2.8 faster than model-based approaches like EAGLE-2/3 and 1.9 faster than model-free approaches such as Token Recycling.

Mixture-of-Experts (MoE) architecture offers enhanced efficiency for Large Language Models (LLMs) with modularized computation, yet its inherent sparsity poses significant hardware deployment challenges, including memory locality issues, communication overhead, and inefficient computing resource utilization. Inspired by the modular organization of the human brain, we propose Mozart, a novel algorithm-hardware co-design framework tailored for efficient training of MoE-based LLMs on 3.5D wafer-scale chiplet architectures. On the algorithm side, Mozartexploits the inherent modularity of chiplets and introduces: (1) an expert allocation strategy that enables efficient on-package all-to-all communication, and (2) a fine-grained scheduling mechanism that improves communication-computation overlap through streaming tokens and experts. On the architecture side, Mozart adaptively co-locates heterogeneous modules on specialized chiplets with a 2.5D NoP-Tree topology and hierarchical memory structure. Evaluation across three popular MoE models demonstrates significant efficiency gains, enabling more effective parallelization and resource utilization for large-scale modularized MoE-LLMs.