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NVIDIA CEO SHOWED A $249 DESKTOP AI COMPUTER THAT CAN RUN LARGE LANGUAGE MODELS LOCALLY. WILD.

Vivek Sen

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86,125 次观看 • 1 天前 •via X (Twitter)

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The Jetson Nano is designed specifically for AI: It can run AI language models locally - without needing cloud connections. Think of it like a Raspberry Pi specifically optimized for AI applications. This $249 device fundamentally changes how AI will develop:

The Jetson Nano is designed specifically for AI: It can run AI language models locally - without needing cloud connections. Think of it like a Raspberry Pi specifically optimized for AI applications. This $249 device fundamentally changes how AI will develop:

GC Cooke

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people think running AI locally requires: → $3,000 MacBook Pro → RTX 4090 → $20/month cloud subscription nvidia just dropped a $249 computer. 67 TOPS. runs llama 3.1-8B locally. no internet. no API. no monthly fee. ever. smaller than your router. costs the same as AirPods. runs the same models you pay $240/year to access via ChatGPT. the local AI era just got a price tag. $249.

people think running AI locally requires: → $3,000 MacBook Pro → RTX 4090 → $20/month cloud subscription nvidia just dropped a $249 computer. 67 TOPS. runs llama 3.1-8B locally. no internet. no API. no monthly fee. ever. smaller than your router. costs the same as AirPods. runs the same models you pay $240/year to access via ChatGPT. the local AI era just got a price tag. $249.

self.dll

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WOW! OPENAI DID IT! They released their first TWO open weights models! This is HUGE news for the entire AI industry. It means you can RIGHT NOW run cutting edge AI models locally on your computer Here's all the benefits and how to set up the models locally yourself:

WOW! OPENAI DID IT! They released their first TWO open weights models! This is HUGE news for the entire AI industry. It means you can RIGHT NOW run cutting edge AI models locally on your computer Here's all the benefits and how to set up the models locally yourself:

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🚨 NVIDIA Introduces Jetson Nano Super > compact AI computer capable of 70-T operations per second > designed for robotics, it supports advanced models, including LLMs, and costs $249

🚨 NVIDIA Introduces Jetson Nano Super > compact AI computer capable of 70-T operations per second > designed for robotics, it supports advanced models, including LLMs, and costs $249

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China just released a desktop automation agent that runs 100% locally. It can run any desktop app, open files, browse websites, and automate tasks without needing an internet connection. 100% Open-Source.

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Recently, PostTrainBench showed how well AI agents can post-train models. Meta Harness showed that the harness itself can improve. What happens if a harness is improvising itself and the improvised harness is post training language models, all in a loop? Viv on this:

Recently, PostTrainBench showed how well AI agents can post-train models. Meta Harness showed that the harness itself can improve. What happens if a harness is improvising itself and the improvised harness is post training language models, all in a loop? Viv on this:

himanshu

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NVIDIA JUST SHOWED HOW A $4,699 BOX CAN KILL A $200 MONTHLY AI BILL AND RUN 200B MODELS FROM YOUR DESK. 00:02 NVIDIA delivers the DGX Spark to developers, researchers, and AI teams, turning what used to require rented datacenter GPUs into a box small enough to hold in one hand. inside is 128GB of unified memory, 4TB of storage, and up to 1 PFLOP of AI compute, enough to run 70B models comfortably and push into the 200B class locally. that means no per-token API bill, no files leaving your machine, and no waiting for someone else’s server every time an agent reads documents, writes code, or runs an automation. two DGX Sparks can even be linked together for models around 400B parameters, while a single unit costs roughly $4,000 to $4,700 instead of renting multi-GPU cloud infrastructure every month. the full local AI ladder starts at $0 with the computer you already own, but NVIDIA just showed what the final rung looks like: a personal AI supercomputer sitting directly on your desk.

NVIDIA JUST SHOWED HOW A $4,699 BOX CAN KILL A $200 MONTHLY AI BILL AND RUN 200B MODELS FROM YOUR DESK. 00:02 NVIDIA delivers the DGX Spark to developers, researchers, and AI teams, turning what used to require rented datacenter GPUs into a box small enough to hold in one hand. inside is 128GB of unified memory, 4TB of storage, and up to 1 PFLOP of AI compute, enough to run 70B models comfortably and push into the 200B class locally. that means no per-token API bill, no files leaving your machine, and no waiting for someone else’s server every time an agent reads documents, writes code, or runs an automation. two DGX Sparks can even be linked together for models around 400B parameters, while a single unit costs roughly $4,000 to $4,700 instead of renting multi-GPU cloud infrastructure every month. the full local AI ladder starts at $0 with the computer you already own, but NVIDIA just showed what the final rung looks like: a personal AI supercomputer sitting directly on your desk.

Gipp 🦅

11,334 次观看 • 14 天前

“In my own company here, we have more than 100 use cases already in production using large language models or agentic AI models,” HP CEO Antonio Neri says.

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Open models are accelerating development and deployment across agentic AI, physical AI, and a growing range of industries. ✨ “We have billions of dollars of supercomputers in operation, so we can develop our open models,” said NVIDIA CEO Jensen Huang in a recent keynote. “That work is starting to attract attention across industries worldwide, as we build frontier AI models in many different domains.” This includes leading open models from NVIDIA, including: ⚙️ NVIDIA Nemotron for agentic AI ⚙️ NVIDIA Cosmos and GR00T for physical AI ⚙️ NVIDIA Alpamayo for autonomous vehicles ⚙️ NVIDIA Clara for biomedical AI Learn more about open source and AI ➡️

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128GB in Framework Desktop means you can run giant models locally like AI at Meta's Llama 4 Scout. This is a 109B MoE model with 17B active parameters. At Q6 this runs at >14 tok/s on LM Studio on Fedora 42!

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Perplexity CEO Aravind Srinivas says the biggest threat to data centers is intelligence that runs locally on your device If models run on your own chip, they can watch your workflow and adapt via "test time training" without data ever leaving your computer "you own it. it's your brain"

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