NVIDIA is reportedly bringing back 2021's RTX 3060 GPU because AI is eating all of the newer cards Everything old is new again? A reputable leaker has indicated that NVIDIA plans on bringing the RTX 3060 back to market, according to reports by and . It first released the GPU at the beginning of 2021 . The leaker Hongxing2020 indicates that NVIDIA will resume production of the 3060 sometime in the next few months. Why is the world's most valuable company reportedly bringing back such an antiquated graphics card?

We present a noise-aware, sensor-specific ensemble approach for robust human activity recognition on the 2nd WEAR Dataset Challenge. Our method leverages the PatchTST transformer architecture, training four independent models-one per inertial sensor location-on a tampered training set whose 1-second sliding windows are augmented to mimic the test-time noise. By aligning the train and test data schemas (JSON-encoded 50-sample windows) and applying randomized jitter, scaling, rotation, and channel dropout, each PatchTST model learns to generalize across real-world sensor perturbations. At inference, we compute softmax probabilities from all four sensor models on the Kaggle test set and average them to produce final labels. On the private leaderboard, this pipeline achieves a macro-F1 substantially above the baseline, demonstrating that test-matched augmentation combined with transformer-based ensembling is an effective strategy for robust HAR under noisy conditions.

Despite demonstrating remarkable performance across a wide range of tasks, large language models (LLMs) have also been found to frequently produce outputs that are incomplete or selectively omit key information. In sensitive domains, such omissions can result in significant harm comparable to that posed by factual inaccuracies, including hallucinations. In this study, we address the challenge of evaluating the comprehensiveness of LLM-generated texts, focusing on the detection of missing information or underrepresented viewpoints. We investigate three automated evaluation strategies: (1) an NLI-based method that decomposes texts into atomic statements and uses natural language inference (NLI) to identify missing links, (2) a Q&A-based approach that extracts question-answer pairs and compares responses across sources, and (3) an end-to-end method that directly identifies missing content using LLMs. Our experiments demonstrate the surprising effectiveness of the simple end-to-end approach compared to more complex methods, though at the cost of reduced robustness, interpretability and result granularity. We further assess the comprehensiveness of responses from several popular open-weight LLMs when answering user queries based on multiple sources.

This project implements a ResNet-based pipeline for land use and land cover (LULC) classification on Sentinel-2 imagery, benchmarked across three heterogeneous GPUs. The workflow automates data acquisition, geospatial preprocessing, tiling, model training, and visualization, and is fully containerized for reproducibility. Performance evaluation reveals up to a 2x training speed-up on an NVIDIA RTX 3060 and a Tesla T4 compared to the Apple M3 Pro baseline, while maintaining high classification accuracy on the EuroSAT dataset. These results demonstrate the feasibility of deploying deep learning LULC models on consumer and free cloud GPUs for scalable geospatial analytics.

The rapid adoption of language models (LMs) across diverse applications has raised concerns about their factuality, i.e., their consistency with real-world facts. We first present VERIFY (Verification and Evidence RetrIeval for FactualitY evaluation), a pipeline to evaluate LMs' factuality in real-world user interactions. VERIFY considers the verifiability of LM-generated content and categorizes content units as supported, unsupported, or undecidable based on Web-retrieved evidence. Importantly, factuality judgment by VERIFY correlates better with human evaluations than existing methods. Using VERIFY, we identify "hallucination prompts" across diverse topics, i.e., those eliciting the highest rates of incorrect (unsupported) and inconclusive (undecidable) LM responses. These prompts form FACTBENCH, a dataset of 1K prompts across 150 fine-grained topics. Our dataset captures emerging factuality challenges in real-world LM interactions and can be regularly updated with new prompts. We benchmark widely-used LMs from GPT, Gemini, and Llama families on FACTBENCH, yielding the following key findings: (i) Proprietary models exhibit better factuality, with decreased performance from Easy to Hard hallucination prompts. (ii) Llama3.1-405B-Instruct shows comparable or lower factual precision than Llama3.1-70B-Instruct across all evaluation methods due to its higher subjectivity that leads to more content labeled as undecidable. (iii) Gemini1.5-Pro shows a significantly higher refusal rate, with over-refusal in 25% of cases.

Transformers are widely used across various applications, many of which yield sparse or partially filled attention matrices. Examples include attention masks designed to reduce the quadratic complexity of attention, sequence packing techniques, and recent innovations like tree masking for fast validation in MEDUSA. Despite the inherent sparsity in these matrices, the state-of-the-art algorithm Flash Attention still processes them with quadratic complexity as though they were dense. In this paper, we introduce Binary Block Masking, a highly efficient modification that enhances Flash Attention by making it mask-aware. We further propose two optimizations: one tailored for masks with contiguous non-zero patterns and another for extremely sparse masks. Our experiments on attention masks derived from real-world scenarios demonstrate up to a 9x runtime improvement. The implementation will be publicly released to foster further research and application.

The GeForce RTX 4060 Ti 8GB brings Nvidia's awesome DLSS 3 mainstream, but offers disappointing value as an upgrade to the 3060 Ti and as a standalone 1080p gaming option in 2023. Technical decisions also make it unappealing for 1440p gamers, unlike its predecessor. At first blush, Nvidia's $399 GeForce RTX 4060 Ti (8GB) looks like it should be a smashing success. Its predecessor, 2020's $399 GeForce RTX 3060 Ti, was one of the best GPUs in a strong RTX 30-series generation, offering impeccable 1080p and spectacular 1440p gaming performance at a reasonable price. The new RTX 4060 Ti sticks to the same price while weaving in Nvidia's latest killer RTX 40-series features, like DLSS 3, Reflex, RTX Video Super Resolution, best-in-class ray tracing, AV1 encoding, stunning power efficiency, and more. Nvidia made technical decisions--reducing core counts and altering the memory subsystem--that allow the GeForce RTX 4060 Ti to scream at 1080p resolution but make it unappealing for 1440p gaming.

ChatGPT can give some impressive results, and also sometimes some very poor advice. But while it's free to talk with ChatGPT in theory, often you end up with messages about the system being at capacity, or hitting your maximum number of chats for the day, with a prompt to subscribe to ChatGPT Plus. Also, all of your queries are taking place on ChatGPT's server, which means that you need Internet and that OpenAI can see what you're doing. Fortunately, there are ways to run a ChatGPT-like LLM (Large Language Model) on your local PC, using the power of your GPU. The oobabooga text generation webui (opens in new tab) might be just what you're after, so we ran some tests to find out what it could -- and couldn't! Getting the webui running wasn't quite as simple as we had hoped, in part due to how fast everything is moving within the LLM space. There are the basic instructions in the readme, the one-click installers, and then multiple guides for how to build and run the LLaMa 4-bit models (opens in new tab).

Intel's long-promised desktop GPUs are finally close to reaching gamers worldwide. As part of its flurry of announcements, Intel has confirmed the Arc A770 GPU will be available in a range of models on October 12th starting at $329. As the price suggests, this is aimed squarely at the GeForce RTX 3060, Radeon RX 6650 XT and other mid-tier video cards -- Intel claims both "1440p gaming performance" and up to 65 percent stronger "peak" ray tracing performance than rivals, although it didn't name specific hardware. Like competitors, Intel is counting as much on AI as it is raw computing power. The Arc A770 supports Xe Super Sampling (XeSS) that, like NVIDIA's DLSS or AMD's FidelityFX Super Resolution, uses AI upscaling to boost frame rates at higher resolutions.

Nvidia's GeForce RTX 3050 delivers great 1080p gaming performance with modern features, including capable ray tracing chops and DLSS. It has plenty of memory and doesn't make any unusual technical compromises, unlike AMD's rival Radeon RX 6500 XT, but that potentially makes it a target for GPU miners--which could mean bad things for price and availability. A year and a half into the latest generation of graphics cards--one plagued by chip shortages, logistics woes, tariffs, crypto demand, and scalpers--we're finally starting to see the first GPUs for PC gamers on a tighter budget. And as the GeForce RTX 3050 we're reviewing today shows, Nvidia and AMD couldn't be going about it any more differently. AMD landed the first strike. The Radeon RX 6500 XT arrived just last week, and AMD made some hard compromises to hit its low $199 price point.