There's a deep, forbidding moat that surrounds Nvidia--and it has nothing to do with hardware. Forgive me for starting with a cliché, a piece of finance jargon that has recently slipped into the tech lexicon, but I'm afraid I must talk about "moats." Popularized decades ago by Warren Buffett to refer to a company's competitive advantage, the word found its way into Silicon Valley pitch decks when a memo purportedly leaked from Google, titled "We Have No Moat, and Neither Does OpenAI," fretted that open-source AI would pillage Big Tech's castle. A few years on, the castle walls remain safe. Apart from a brief bout of panic when DeepSeek first appeared, open-source AI models have not vastly outperformed proprietary models.

A.1 Dataset Details & Evaluation Metrics As stated earlier, the main application of Extreme Multi-label Text Classification is in e-commerce - product recommendation and dynamic search advertisement - and in document tagging, where the objective of an algorithm is to correctly recommend/advertise among the top-k slots. Thus, for evaluation of the methods, we use precision at k (denoted by P@k), and its propensity scored variant (denoted by PSP@k) [17]. These are standard and widely used metrics by the XMC community [4]. Since P@k treats all the labels equally, it doesn't reveal the performance of the model on tail labels. However, because of the long-tailed distribution in XMC datasets, one of the main challenges is to predict tail labels correctly, which may be more valuable and informative compared to head classes.

A.1 Datasets fMoWRGBFunctional Map of the World (fMoW) [17] is a dataset of high-resolution satellite image time series across the world, with a task of classification among 62 architecture categories such as airport, shipyard, and zoo. The license is provided here 2. Co-located images of different timestamps, or sequences, are provided in fMoW. They are of different length, and around 60% of the samples have length larger than 2. Readers can refer to the fMoW paper [17] for statistics on the distribution of sequence lengths. We construct a temporal version of fMoW by randomly associating every single image with two images of the same location but of different timestamps if possible. For a given spatial location loc, we define Tloc as the number of temporally distinct snapshots present in the dataset. We crop surface reflectance images from the Sentinel-2 (ESA) satellite (courtesy of the U.S. Geological Survey), consisting of 90-day composites of images at the same locations as fMoW images (to reduce the impacts of cloud coverage). At each fMoW datapoint location, we collect a time series of Sentinel-2 images, using the provided geo-coordinate bounding boxes.

Weight-Decomposed Low-Rank Adaptation (DoRA) extends LoRA by decoupling weight magnitude from direction, but its forward pass requires the row-wise norm of W + sBA, a computation that every major framework we surveyed implements by materializing the dense [d_out, d_in] product BA. At d_in = 8192 and rank r = 384, a single module's norm requires about 512 MB of transient working memory in bf16, making high-rank DoRA costly and often infeasible on common single-GPU setups once hundreds of adapted modules and checkpointing are involved. We present two systems contributions. A factored norm decomposes the squared norm into base, cross, and Gram terms computable through O(d_out r + r^2) intermediates, eliminating the dense product. Fused Triton kernels collapse the four-kernel DoRA composition into a single pass, reducing memory traffic by about 4x and using a numerically stable form that avoids catastrophic cancellation in the near-unity rescaling regime where magnitude scales concentrate in practice. Across six 8-32B vision-language models (VLMs) on three NVIDIA GPUs (RTX 6000 PRO, H200, B200) at r = 384 in bf16, the fused implementation is 1.5-2.0x faster than Hugging Face PEFT's DoRA implementation for inference and 1.5-1.9x faster for gradient computation (optimizer step excluded), with up to 7 GB lower peak VRAM. Microbenchmarks on six GPUs spanning four architecture generations (L40S, A100, RTX 6000 PRO, H200, B200, B300) confirm 1.5-2.7x compose-kernel speedup. Final-logit cosine similarity exceeds 0.9999 across all model/GPU pairs, and multi-seed training curves match within 7.1 x 10^-4 mean per-step loss delta over 2000 steps.

Attention, as a core layer of the ubiquitous Transformer architecture, is the bottleneck for large language models and long-context applications.

As text-to-image (T2I) synthesis models increase in size, they demand higher inference costs due to the need for more expensive GPUs with larger memory, which makes it challenging to reproduce these models in addition to the restricted access to training datasets. Our study aims to reduce these inference costs and explores how far the generative capabilities of T2I models can be extended using only publicly available datasets and open-source models. To this end, by using the de facto standard text-to-image model, Stable Diffusion XL (SDXL), we present three key practices in building an efficient T2I model: (1) Knowledge distillation: we explore how to effectively distill the generation capability of SDXL into an efficient U-Net and find that self-attention is the most crucial part.

As large language models gain widespread adoption, running them efficiently becomes a crucial task. Recent works on LLM inference use speculative decoding to achieve extreme speedups. However, most of these works implicitly design their algorithms for high-end datacenter hardware. In this work, we ask the opposite question: how fast can we run LLMs on consumer machines? Consumer GPUs can no longer fit the largest available models and must offload them to RAM or SSD. With parameter offloading, hundreds or thousands of tokens can be processed in batches within the same time as just one token, making it a natural fit for speculative decoding. We propose SpecExec (Speculative Execution), a simple parallel decoding method that can generate up to 20 tokens per target model iteration for popular LLM families. SpecExec takes the most probable continuations from the draft model to build a cache tree for the target model, which then gets validated in a single pass. Using SpecExec, we demonstrate inference of 50B+ parameter LLMs on consumer GPUs with RAM offloading at 4--6 tokens per second with 4-bit quantization or 2--3 tokens per second with 16-bit weights.

PCWorld reports Microsoft is embedding AI into DirectX with new tools called DirectX Linear Algebra and DirectX Compute Graph Compiler to revolutionize game rendering. Major chip makers AMD, Intel, and Nvidia support these AI initiatives, potentially allowing integrated GPUs to compete with discrete graphics cards in gaming performance. These technologies enable dynamic shader creation, neural texture compression, and advanced upscaling that could democratize high-end graphics features like path tracing across different hardware. Games are increasingly being rendered using AI, so Microsoft is bringing AI into the way future graphics chips will render games. Microsoft introduced DirectX Linear Algebra as well as the DirectX Compute Graph Compiler into its DirectX programming interface on Thursday, with previews of each technology due later this year.