Modern multimodal large language models (MLLMs) can reason over hour-long video, yet their key-value (KV) cache grows linearly with time--quickly exceeding the fixed memory of phones, AR glasses, and edge robots. Prior compression schemes either assume the whole video and user query are available offline or must first build the full cache, so memory still scales with stream length. InfiniPot-V is the first training-free, query-agnostic framework that enforces a hard, lengthindependent memory cap for streaming video understanding. During video encoding it monitors the cache and, once a user-set threshold is reached, runs a lightweight compression pass that (i) removes temporally redundant tokens via Temporal-axis Redundancy (TaR) metric and (ii) keeps semantically significant tokens via Value-Norm (VaN) ranking. Across four open-source MLLMs and four long-video and streaming-video benchmarks, InfiniPot-V cuts peak GPU memory by up to 94%, sustains real-time generation, and matches or surpasses full-cache accuracy--even in multi-turn dialogues. By dissolving the KV cache bottleneck without retraining or query knowledge, InfiniPot-V closes the gap for on-device streaming video assistants.

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As the economic and environmental costs of training and deploying large vision or language models increase dramatically, analog in-memory computing (AIMC) emerges as a promising energy-efficient solution. However, the training perspective, especially its training dynamics, is underexplored. In AIMC hardware, the trainable weights are represented by the conductance of resistive elements and updated using consecutive electrical pulses. While the conductance changes by a constant in response to each pulse, in reality, the change is scaled by asymmetric and non-linear response functions, leading to a non-ideal training dynamics. This paper provides a theoretical foundation for gradient-based training on AIMC hardware with nonideal response functions.

The computational sparsity of Mixture-of-Experts (MoE) models enables sublinear growth in compute cost as model size increases, thus offering a scalable path to training massive neural networks. However, existing implementations suffer from low GPU utilization, significant latency overhead, and a fundamental inability to leverage task locality, primarily due to CPU-managed scheduling, host-initiated communication, and frequent kernel launches. To overcome these limitations, we develop FlashMoE, a fully GPU-resident MoE operator that fuses expert computation and inter-GPU communication into a single persistent GPU kernel. FlashMoE enables fine-grained pipelining of dispatch, compute, and combine phases, eliminating launch overheads and reducing idle gaps. Unlike existing work, FlashMoE obviates bulk-synchronous collectives for one-sided, device-initiated, inter-GPU (R)DMA transfers, thus unlocking payload efficiency, where we eliminate bloated or redundant network payloads in sparsely activated layers. When evaluated on an 8-H100 GPU node with MoE models having up to 128 experts and 16K token sequences, FlashMoE achieves up to 9 higher GPU utilization, 6 lower latency, 5.7 higher throughput, and 4 better overlap efficiency compared to state-of-the-art baselines--despite using FP32 while baselines use FP16. FlashMoE shows that principled GPU kernel-hardware co-design is key to unlocking the performance ceiling of large-scale distributed ML.

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PCWorld reports that rising RAM and storage prices are driving hardware vendors to re-release older components like AMD's Ryzen 7 5800X3D and GeForce RTX 30-series cards. This trend matters for PC builders seeking affordable alternatives as memory shortages and cost increases make newer hardware less accessible. The shift encourages enthusiasts to find creative solutions with existing components and embrace the joy of tinkering despite market challenges. Your weekly edition of The Full Nerd has arrived, and there's a new face on the team: mine!

When you purchase through links in our articles, we may earn a small commission. I can't stop using this website that lets me drive through cities around the world Drive and Listen combines street-level video with live local radio for a surprisingly immersive experience--even if it's only from your desktop. Drive and Listen might not sound like it, but it's strangely relaxing once you try it. The site provides exactly the experience the name suggests: Pick a city anywhere in the world, press play, and sit back as you ride along through the streets listening to local radio stations. The site was created during the pandemic by a student in Turkey who missed traveling and wanted a way to reconnect with places beyond his own neighborhood.

Find the right wearable for your lifestyle, workouts, and goals. Like every piece of gear you wear on your body day in and day out, fitness trackers are incredibly personal. The right tracker for you should be comfortable, accurate, and tailored to your lifestyle, including your preferred workouts and health goals. Do you bike, row, or strength train? Do you run on trails for hours at a time, or do you just want a reminder to stand up every hour? Do you want to wear it on your wrist or your finger, or tuck it into your sports bra? No matter what your needs are, there's never been a better time to find a powerful, sophisticated tool to help optimize your workouts or jump-start your routine. We test dozens of fitness trackers every year while running, climbing, hiking, or just doing workout videos on our iPads at night, to bring you these picks. For more wearables, check out our guides to the Best Smartwatches, Best Smart Rings, and Best Sleep Trackers . Garmin makes some of the most accurate fitness trackers on the market, and the Vivoactive 6 is the best midrange option for most people.

Quantum many-body problems are central to various scientific disciplines, yet their ground-state properties are intrinsically challenging to estimate.