825건 · 관련도순
Cut background heat and noise on this machine: audit scheduled tasks by runs per hour, autostart entries, GPU holders, power plan and top CPU, then propose disable or stretch candidates, decide line by line with the operator and apply only approved lines. Triggers: /deflate, fan is loud, laptop runs hot, what can I turn off, machine slow from background load.
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MegaTrain Full Precision Training of 100B+ Parameter LLMs on a Single GPU A RAM-centric architecture that stores parameters in host memory and treats
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August 16, 2026 12 min read Nvidia AI Infrastructure GPUs FinOps Wall Street Nvidia $500B AI Compu
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It took several weeks and dozens of GPUs to train the model on 40 years of historical weather data from the MERRA-2 is NASA’s harmonized dataset of sa
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Use when the design concept calls for Three.js or React Three Fiber 3D, WebGL/WebGPU, Rive, Spline, Theatre.js, SVG, canvas, shaders, particle systems, or other immersive creative-web techniques.
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Use when the user provides a URL from AMD / ROCm official sources (github.com/AMD/*, github.com/ROCm/*, github.com/ROCmSoftwarePlatform/*, rocm.docs.amd.com, amd.com/…/rocm, gpuopen.com). Prefer this over generic GitHub for official ROCm URLs. Generates a hands-on tutorial-following project with no collaborator roles — only the user and the Instructor.
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Run PyTorch training across GPUs with minimal changes.
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PyTorch deep learning development with transformers, diffusion models, and GPU optimization.
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Morphs and deformation: production-level blendshapes, GPU morphing, state. Use when implementing, reviewing or testing morphs and deformation for React, Vite, Next.js or Perfect_AI projects.
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WAAPI and compositing: production-level GPU-friendly transform/opacity, offscreen pausing. Use when implementing, reviewing or testing waapi and compositing for React, Vite, Next.js or Perfect_AI projects.
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Core Web Vitals budgets: production-level LCP, CLS, INP, GPU scheduling. Use when implementing, reviewing or testing core web vitals budgets for React, Vite, Next.js or Perfect_AI projects.
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Use when working with Azure Container Instances — azure Container Instances management covering container group inventory, container status and restart counts, CPU and memory utilization, networking configuration, log retrieval, and GPU allocation tracking. Use for ACI workload monitoring and troubleshooting.
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Use when working with Baseten — baseten ML deployment platform management covering model inventory, deployment status, autoscaling configuration, inference call history, GPU allocation, environment management, and performance metrics. Use for comprehensive Baseten workspace assessment and ML serving optimization.
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Use when working with Banana Dev — banana.dev ML inference platform management covering model inventory, deployment status, API call history, GPU allocation, scaling configuration, build logs, and latency metrics. Use for comprehensive Banana.dev model deployment assessment and inference performance analysis.
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Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. Supports NLP (text classification, translation, summarization), computer vision (image classification, object detection), audio (speech recognition, audio classification), and multimodal tasks. Works in browsers and server-side runtimes (Node.js, Bun, Deno) with WebGPU/WASM using pre-trained models from Hugging Face Hub.
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Run evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware. Use for backend selection, local GPU evals, and choosing between vLLM / Transformers / accelerate. Not for HF Jobs orchestration, model-card PRs, .eval_results publication, or community-evals automation.
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Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.
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Build and publish a Gradio demo on Hugging Face Spaces for a user-provided LoRA. Use when someone asks to create, generate, ship, or publish a Space, demo, Gradio app, or playground for a LoRA — including LoRAs for Qwen-Image, Qwen-Image-Edit, LTX-Video, Wan, FLUX, SDXL, or other diffusion base models. Also triggers when someone describes a LoRA they trained or hosts on the Hub and wants to share it. Covers picking the right base pipeline and `diffusers` inference recipe, designing a UI tailored to the LoRA's task and inputs (Union/multi-task control, edit, video, image, etc.), respecting model-card recommendations (trigger words, steps, guidance, LoRA scale, example inputs), and shipping to ZeroGPU hardware as a private Space by default.
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GPU monitoring with risk intelligence. Local + cloud fleet monitoring, health tracking, proactive alerts, and AI-powered fleet analytics.
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Integrate and optimize Core ML models in iOS apps for on-device machine learning inference. Covers model loading (.mlmodelc, .mlpackage), predictions with auto-generated classes and MLFeatureProvider, compute unit configuration (CPU, GPU, Neural Engine), MLTensor, VNCoreMLRequest, MLComputePlan, multi-model pipelines, and deployment strategies. Use when loading Core ML models, making predictions, configuring compute units, or profiling model performance.
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Build alternative browser engines using BrowserEngineKit. Use when developing a non-WebKit browser engine for iOS in the EU, managing web content rendering processes, configuring GPU and networking processes for browser functionality, checking device eligibility for alternative engines, or working with BrowserEngineKit entitlements.
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Web browser game development principles. Framework selection, WebGPU, optimization, PWA.
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Investigate model specifications, requirements, and evaluation strategy. Use when researching a model before benchmarking: reading HuggingFace model cards, estimating VRAM requirements, selecting GPU providers, and determining evaluation approach. Triggers on "model research", "investigate model", "model info", "VRAM estimate", "which provider", "model card".
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Autonomous model validation and benchmarking. Investigates any ML model (LLM, image gen, TTS, time series, etc.), runs it on GPU cloud, evaluates quality and performance, and generates HTML reports. Use when user asks to verify, benchmark, evaluate, or test a model. Triggers on "verify model", "benchmark", "evaluate model", "test model", "run benchmark", "model evaluation", "モデルを検証", "ベンチマーク", "モデルを試して".
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