graphics processing unit
PulseAugur coverage of graphics processing unit — every cluster mentioning graphics processing unit across labs, papers, and developer communities, ranked by signal.
- used by Vulkan 90%
- used by central processing unit 70%
- competes with Tensor Processing Unit 70%
- competes with application-specific integrated circuit 70%
- competes with Cerebras Systems 70%
- used by H.1000 Gnome 70%
- used by SemiAnalysis 70%
- uses data processing unit 70%
- used by Fp8 70%
- used by Dohuk Polytechnic University 70%
- competes with central processing unit 50%
- uses central processing unit 50%
29 day(s) with sentiment data
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DataLoader bottlenecks can starve GPUs during PyTorch training
A slow PyTorch training job may not be due to the model's complexity but rather the data loading process. The article explains how to identify if your GPU is being starved of data by a slow DataLoader. It suggests metho…
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LLM inference PC build: User asks if CPU/RAM matter with powerful GPUs
A user on the r/LocalLLaMA subreddit is seeking advice on building a PC for large language model (LLM) inference. They want to prioritize GPU spending and minimize costs for other components. The core question is whethe…
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New AI methods generate 3D brain MRI data efficiently
Researchers have developed two new methods, WaveDiT and FlowLet, for synthesizing 3D brain MRI data. These techniques utilize wavelet transforms and flow matching to generate high-fidelity images efficiently, even on a …
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DeepSeek plans GW-scale data centers to support AI ambitions
DeepSeek is actively recruiting for an IDC Design and Planning Engineer, signaling a significant move towards building its own large-scale data centers. This initiative, likely fueled by a recent substantial funding rou…
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ChatGPT set for largest-ever upgrade; GPU futures planned
ChatGPT is poised for its most significant update, moving beyond simple chat functionalities. This upgrade is expected to be the largest in the model's history. Concurrently, financial institutions like Goldman Sachs an…
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User asks about dual-GPU performance for local LLMs
A user on Reddit's r/LocalLLaMA subreddit is seeking advice on optimizing hardware for running large language models locally. They are currently able to run a 16 billion parameter model with Q4 quantization on a single …
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AI safety expert questions value of weak regulations
This post explores the potential benefits and drawbacks of implementing weak AI safety regulations. The author argues that while strong regulations are ideal for preventing existential risks from superintelligent AI, we…
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LLMs run in browser, boosting privacy and local processing
New developments are enabling large language models (LLMs) to run directly within web browsers, addressing privacy concerns associated with cloud-based services. Projects like SmolLM2 are creating smaller, more efficien…
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AI agents drive surge in data center CPU demand, altering infrastructure ratios
The demand for data center CPUs has significantly increased, driven by the rise of AI agents. While GPUs have been the primary focus for AI workloads, CPUs are now recognized as crucial for handling the "everything else…
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Anatrix unveils analog chip for single-step matrix inverse solutions
Anatrix, a Chinese startup, is developing analog computing chips that can solve matrix inverse problems in a single step, unlike GPUs which require billions of operations. Their approach focuses on achieving high precis…
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LLM inference efficiency explored on edge devices and cloud GPUs
Two new research papers explore the challenges of running large language models (LLMs) efficiently. The first paper investigates the performance trade-offs of deploying LLMs on edge devices like smartphones and speciali…
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New GPU solver AFSAT enhances pseudo-Boolean satisfiability
Researchers have developed Accelerated Fourier SAT (AFSAT), a new GPU-accelerated solver for pseudo-Boolean satisfiability problems. AFSAT builds upon a previous proof-of-concept, FastFourierSAT, by engineering a fully …
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AI demand triggers MLCC super cycle, causing shortages
The demand for AI computing power is driving a significant upswing in the MLCC (multilayer ceramic capacitor) market, with some analysts predicting a "super cycle." MLCCs are now a major cost component in AI servers, an…
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llama-server router allocates CUDA context on all GPUs, causing OOM errors
A user on the r/LocalLLaMA subreddit is encountering an issue with the llama-server router mode where each model instance, even when pinned to a specific GPU, allocates a CUDA context on all available GPUs. This behavio…
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Google launches Colab CLI for remote terminal-based AI development
Google has released an open-source command-line interface (CLI) for Colab, enabling developers and AI agents to execute Python code on remote GPUs and TPUs directly from their terminals. This tool allows for the provisi…
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MLOps training shifts to carbon-aware GPU scheduling
A new approach to training machine learning models focuses on minimizing their environmental impact by scheduling GPU workloads around periods of lower electricity carbon intensity. This method aims to reduce the signif…
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InferBench app simplifies local LLM performance testing
A new open-source desktop application called InferBench has been released to help users determine which large language models (LLMs) can run on their local GPUs and at what speed. The tool automates the process of downl…
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China's AI boom fuels surge in private securities filings and material costs
The number of private securities products filed in China has increased by nearly 50% in the first five months of the year, reaching 6,530 products compared to 4,378 in the same period last year. This surge is partly dri…
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TorchKM library accelerates kernel machine learning on GPUs
Researchers have developed TorchKM, an open-source library designed to accelerate kernel machine learning tasks on GPUs. The library offers a scikit-learn-compatible API and leverages GPU-friendly linear algebra to spee…
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Kubernetes operators enable scale-to-zero for LLM serving
New Kubernetes operators are emerging to address the cost of running large language models, particularly the issue of idle GPUs burning money. Hearth, an alpha-stage operator, allows users to declaratively serve open-so…