You don’t “run a model”
You run Kernels The model is just a graph The Inference Engine is scheduler / optimizer / executor But the actual work? That happens in the Kernels – MatMul Kernels
– Attention Kernels
– RMSNorm Kernels
– KV cache Kernels
– Quantized linear Kernels
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MACHINE LEARNING
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Kernels Are the Actual Work in Model Inference
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AI Weather Startup Outperforms Government Agencies in Forecasting
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This #AI weather startup is out-forecasting government agencies
by Tim Fernholz @TechCrunch Learn more: https://
bit.ly/4u8k1p0 #ClimateTech #FutureTech #Innovation #Technology #EmergingTech -
PixelDiT achieves state-of-the-art FID score on ImageNet 256
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Generating directly in pixels has been explored before, but PixelDiT pushes the approach to a new high. It scored 1.61 FID on ImageNet 256, making it state-of-the-art among pixel-space generative models and competitive with the best latent diffusion models. It also keeps fine
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PixelDiT from NVIDIA Research: Best Paper Finalist at CVPR2026
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Selected as a best paper finalist at #CVPR2026: PixelDiT from NVIDIA Research
— NVIDIA AI (@NVIDIAAI) 5 juin 2026
In most image generation models, a pretrained autoencoder compresses the image before any diffusion happens, causing quality loss that accumulates across the entire pipeline.
PixelDiT, or Pixel… pic.twitter.com/P4jb8Pva91Selected as a best paper finalist at #CVPR2026: PixelDiT from NVIDIA Research In most image generation models, a pretrained autoencoder compresses the image before any diffusion happens, causing quality loss that accumulates across the entire pipeline. PixelDiT, or Pixel
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CEO Rodrigo Liang discusses heterogeneous AI systems and token speed on CNBC
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On CNBC, our CEO @RodrigoLiang talks about the future of heterogeneous AI systems, why token speed and energy efficiency matter for agentic inference, and what’s ahead for us over the next 12 months
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AI writing will soon be indistinguishable from human writing
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Also going to add, that it’s almost certainly a fact that you won’t be able to tell AI writing apart from human writing pretty soon
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Elon leasing LLMs contradicts ‘scale is all you need’
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If scale was “all you need”, Elon would be hoarding LLMs, not leasing them.
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Erdős test: Can AI create conjectures mathematicians will solve?
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The Erdős test: can Al make conjectures that human mathematicians will try to solve?
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2026 Physics and AI Conference: ML for Discovery, Physics Principles for AI
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Physics presents distinctive AI problems that go beyond computer vision or NLP applications. How can ML accelerate discovery, and which physics principles can improve AI? Explore these and more at the 2026 Conference on Physics and AI next week: https://
datascience.stanford.edu/events/center-
decoding-universe/c4du-annual-conference/2026-conference-physics-and-ai-pai26
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MIT framework for self-revising AI expands scientific vocabulary
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AI scientists may be moving from search to real discovery.
— Chubby♨️ (@kimmonismus) 5 juin 2026
A new MIT paper proposes a framework for self-revising AI systems that don’t just explore a fixed scientific vocabulary, but can expand the vocabulary itself, introducing new variables, tools, verifiers, and model… https://t.co/fvg1K5aOTo pic.twitter.com/z2moBLejZcAI scientists may be moving from search to real discovery. A new MIT paper proposes a framework for self-revising AI systems that don’t just explore a fixed scientific vocabulary, but can expand the vocabulary itself, introducing new variables, tools, verifiers, and model