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Archive / page 16 of 49

Latest articles, page 16

  1. Matryoshka Representation Learning: Mathematical Foundations, Nested Subspace Optimization, and Adaptive Dimension Truncation in Modern Embedding Systems

    In modern deep learning systems, embedding vectors serve as the foundational intermediate representation for search, recommendation, retrieval-augmented generation (RAG), and zero-shot classification. Historically, these representations have been rigid: a model trained to produce 1536-dimensional or 2048-dimensional vectors outputs a fixed-width array for every input. Downstream systems must ingest, store, index, and compute distance metrics across the entire dimensional width, regardless of whe

    AI
    8 min read

  2. DeepSeek Generates $70.7M in Revenue with $106M Net Loss in First Seven Months of 2026

    Hangzhou-based artificial intelligence laboratory DeepSeek generated approximately 475 million yuan ($70.7 million) in revenue and recorded a net loss of $106 million during the first seven months of 2026, according to financial figures reported by The Information. The performance marks a roughly tenfold revenue surge compared to the lab's full-year 2025 revenue, alongside a modest contraction in net burn from the $139 million net loss reported for all of 2025. The disclosures provide a rare ac

    AI
    3 min read

  3. Activation-Aware Weight Quantization (AWQ): Mathematical Foundations, Salient Weight Protection, and Hardware-Efficient Low-Bit Inference

    Large language model inference during autoregressive generation is overwhelmingly memory bandwidth bound. While the prefill phase processes multiple prompt tokens in parallel with high arithmetic intensity, the token generation phase computes matrix-vector multiplications ($M=1$) for each sequential token. In this regime, the GPU spends the vast majority of its cycle budget streaming model parameters from High Bandwidth Memory (HBM) or GDDR into SRAM rather than performing floating-point arithme

    AI
    7 min read

  4. Intel Details 256-Core Xeon 7 and 480GB Crescent Island Inference GPU at Hot Chips 2026

    At the Hot Chips 2026 conference, Intel outlined architectural disclosures for three upcoming computing platforms tailored for AI workflows: the Xeon 7 enterprise processor (codename Diamond Rapids), the Crescent Island data center inference GPU, and the Wildcat Lake client processor (Intel Core Series 3). The announcements detail Intel's shift toward modular multi-die packaging, open chiplet interconnect standards, and expanded on-chip memory to meet the computational demands of multi-agent AI

    AI
    3 min read

  5. GPTQ: Mathematical Foundations, Optimal Brain Surgeon Inversion, and Second-Order Error Minimization in LLM Quantization

    Large language model inference during autoregressive generation is overwhelmingly memory-bandwidth bound. For batch size 1 decoding, each generated token requires streaming every parameter of a model from High Bandwidth Memory (HBM) into GPU SRAM and Tensor Cores. A 70-billion parameter model in 16-bit precision (FP16 or BF16) requires roughly 140 GB of VRAM, exceeding the capacity of a single 80 GB NVIDIA A100 or H100 GPU and demanding multi-GPU tensor parallelism solely to hold the model weigh

    AI
    14 min read

  6. AM Intelligence Orders 9,000 Nvidia Vera Rubin Systems for $8B AI Infrastructure Project

    Indian AI infrastructure platform AM Intelligence (AMI) has placed a binding purchase order for 9,000 Nvidia Vera Rubin computing systems. The procurement represents one of the earliest hyperscale commitments for Nvidia's next-generation Rubin architecture across Asia and anchors an $8 billion capital expenditure initiative to build 1 gigawatt (GW) of dedicated AI computing capacity. The first phase of the deployment will take place at AMI's upcoming data center facility in Hyderabad, India. Th

    AI
    2 min read

  7. LLM Evaluation Frameworks in Production: Comparing Promptfoo, DeepEval, Ragas, and Inspect Architecture, Metric Calibration, and Quality Gate Economics

    Testing large language model applications in production requires shifting from deterministic software unit tests to probabilistic evaluation harnesses. Traditional software engineering relies on binary assertions (assert output == expected), but generative models exhibit non-deterministic outputs, variable token distributions, and nuanced semantic drift across prompt revisions, model updates, and temperature configurations. To prevent regressions and quantify system capabilities before deployme

    Evaluation
    7 min read

  8. SmoothQuant: Mathematical Foundations, Per-Channel Outlier Migration, and Hardware-Efficient W8A8 Inference in Large Language Models

    SmoothQuant: Mathematical Foundations, Per-Channel Outlier Migration, and Hardware-Efficient W8A8 Inference in Large Language Models Serving large language models (LLMs) in production environments presents two distinct hardware bottlenecks. During the autoregressive generation (decode) phase with small batch sizes, inference is memory-bandwidth bound, as billions of parameters must be streamed from High Bandwidth Memory (HBM) to on-chip SRAM for every generated token. Conversely, during the pro

    Quantization
    12 min read

  9. Digs Raises $25.3M Series A Led by Builders FirstSource for Residential Construction AI

    Digs, a startup developing AI software for residential construction management, has raised a $25.3 million Series A funding round led by building materials supplier Builders FirstSource. Alongside the equity investment, the two companies entered into a five-year commercial partnership to deploy Digs' document intelligence and digital twin platform across Builders FirstSource's distribution network. The Series A brings Digs' total funding to more than $47 million, following seed and pre-Series A

    Enterprise AI
    2 min read

  10. XPeng Robotics Raises $900M at $6.3B Valuation to Scale IRON Humanoid Production

    XPeng announced that its robotics subsidiary has secured over $900 million in private capital at a post-money valuation exceeding $6.3 billion. The transaction marks the largest single-round private financing in China's physical AI and humanoid robotics sector to date. The funding round was led by IDG Capital, with participation from Gaorong Ventures as well as strategic backing from internet conglomerates Tencent and Alibaba. Concurrently, XPeng Chairman and Chief Executive Officer He Xiaopeng

    Physical AI
    2 min read