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Receipts in video, see it float at ~100-150 while coding the fluctuations were for task and context switching of the model. This thing rips through code! A Single NVIDIA AI DGX Spark ⚡️

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10,822 views • 3 months ago •via X (Twitter)

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From rockets to AI. Nine years after the original NVIDIA DGX-1 handoff, Jensen Huang delivered a brand-new DGX Spark to Elon Musk at Starbase. 🚀✨ The DGX Spark is a petaflop powerhouse built for creators, researchers, and developers — a desktop-sized supercomputer with five times the performance of the first DGX-1. Filmed at SpaceX’s rocket factory in Texas, this moment captures a conversation between the two visionariesI. ➡️ 📹 #SparkSomethingBig

NVIDIA

3,159,905 views • 11 months ago

NVIDIA DGX Spark is the most powerful personal AI supercomputer right now. I am running AI agents working for me 24/7 while keeping my data 100% private. In this video I cover the full setup, real benchmarks and tradeoffs. Here's the honest breakdown:

NVIDIA DGX Spark is the most powerful personal AI supercomputer right now. I am running AI agents working for me 24/7 while keeping my data 100% private. In this video I cover the full setup, real benchmarks and tradeoffs. Here's the honest breakdown:

Shruti

122,548 views • 1 month ago

When NVIDIA CEO Jensen Huang presented the DGX Spark, then he recalled how, back in 2016, he unveiled the world’s first AI supercomputer, the DGX-1, and how Elon Musk, then at OpenAI, became its first customer. The DGX Spark is far more advanced, delivering five times the computational power of the DGX-1.

When NVIDIA CEO Jensen Huang presented the DGX Spark, then he recalled how, back in 2016, he unveiled the world’s first AI supercomputer, the DGX-1, and how Elon Musk, then at OpenAI, became its first customer. The DGX Spark is far more advanced, delivering five times the computational power of the DGX-1.

SMX 🇺🇸

13,315,820 views • 10 months ago

Super cool to see Jensen NVIDIA showcasing Reachy Mini at his #CES26 keynote. Paired with a DGX Spark & Brev, it can make the perfect local home AI robotics setup!

Super cool to see Jensen NVIDIA showcasing Reachy Mini at his #CES26 keynote. Paired with a DGX Spark & Brev, it can make the perfect local home AI robotics setup!

clem 🤗

75,599 views • 8 months ago

CHINA JUST DROPPED AN AI CODING MODEL WITH A 1M CONTEXT WINDOW. And I connected it to Claude Code to see what it could actually do. Meet GLM-X Preview On paper, a few things immediately stood out: → 1M context window → Agentic coding capabilities → Works inside Claude Code → Designed for large-scale coding and reasoning workflows But specs don't matter much if the model can't deliver in practice. So I gave it a real-world task. THE TEST One prompt: > Build a modern AI lead generation dashboard using React and Tailwind CSS. Requirements: → Dark mode → Analytics dashboard → Lead table → Email outreach section → Responsive design → Production-ready component structure Instead of generating a few snippets, it planned the architecture, generated the dashboard components, created the Tailwind configuration, and walked through the implementation requirements. What impressed me most wasn't the code itself. It was how well it maintained context throughout the workflow. That's where a 1M context window starts becoming useful. Less time re-explaining requirements. Less context loss. More room for complex projects. The AI coding race is getting very interesting. And it's no longer just GPT, Claude, and Gemini competing for attention. Results from my test below 👇

CHINA JUST DROPPED AN AI CODING MODEL WITH A 1M CONTEXT WINDOW. And I connected it to Claude Code to see what it could actually do. Meet GLM-X Preview On paper, a few things immediately stood out: → 1M context window → Agentic coding capabilities → Works inside Claude Code → Designed for large-scale coding and reasoning workflows But specs don't matter much if the model can't deliver in practice. So I gave it a real-world task. THE TEST One prompt: > Build a modern AI lead generation dashboard using React and Tailwind CSS. Requirements: → Dark mode → Analytics dashboard → Lead table → Email outreach section → Responsive design → Production-ready component structure Instead of generating a few snippets, it planned the architecture, generated the dashboard components, created the Tailwind configuration, and walked through the implementation requirements. What impressed me most wasn't the code itself. It was how well it maintained context throughout the workflow. That's where a 1M context window starts becoming useful. Less time re-explaining requirements. Less context loss. More room for complex projects. The AI coding race is getting very interesting. And it's no longer just GPT, Claude, and Gemini competing for attention. Results from my test below 👇

Md Riyazuddin

31,199 views • 3 months ago

Unsloth AI and NVIDIA are Revolutionizing Local LLM Fine-Tuning: From RTX Desktops to DGX Spark Fine-tune popular AI models faster with Unsloth on NVIDIA RTX AI PCs such as GeForce RTX desktops and laptops to RTX PRO workstations and the new DGX Spark to build personalized assistants for coding, creative work, and complex agentic workflows. The landscape of modern AI is shifting. We are moving away from a total reliance on massive, generalized cloud models and entering the era of local, agentic AI. Whether it is tuning a chatbot to handle hyper-specific product support or building a personal assistant that manages intricate schedules, the potential for generative AI on local hardware is boundless. However, developers face a persistent bottleneck: How do you get a Small Language Model (SLM) to punch above its weight class and respond with high accuracy for specialized tasks? The answer is Fine-Tuning, and the tool of choice is Unsloth. Unsloth provides an easy and high-speed method to customize models. Optimized for efficient, low-memory training on NVIDIA GPUs, Unsloth scales effortlessly from GeForce RTX desktops and laptop all the way to the DGX Spark, the world’s smallest AI supercomputer...... Full analysis: NVIDIA NVIDIA AI NVIDIA AIDev NVIDIAnewsroom Unsloth AI Unsloth

Unsloth AI and NVIDIA are Revolutionizing Local LLM Fine-Tuning: From RTX Desktops to DGX Spark Fine-tune popular AI models faster with Unsloth on NVIDIA RTX AI PCs such as GeForce RTX desktops and laptops to RTX PRO workstations and the new DGX Spark to build personalized assistants for coding, creative work, and complex agentic workflows. The landscape of modern AI is shifting. We are moving away from a total reliance on massive, generalized cloud models and entering the era of local, agentic AI. Whether it is tuning a chatbot to handle hyper-specific product support or building a personal assistant that manages intricate schedules, the potential for generative AI on local hardware is boundless. However, developers face a persistent bottleneck: How do you get a Small Language Model (SLM) to punch above its weight class and respond with high accuracy for specialized tasks? The answer is Fine-Tuning, and the tool of choice is Unsloth. Unsloth provides an easy and high-speed method to customize models. Optimized for efficient, low-memory training on NVIDIA GPUs, Unsloth scales effortlessly from GeForce RTX desktops and laptop all the way to the DGX Spark, the world’s smallest AI supercomputer...... Full analysis: NVIDIA NVIDIA AI NVIDIA AIDev NVIDIAnewsroom Unsloth AI Unsloth

Marktechpost AI Dev News ⚡

31,551 views • 9 months ago

INTEL JUST QUIETLY BUILT THE CHEAPEST PATH TO 128GB OF LOCAL AI VRAM, AND IT BEATS NVIDIA'S DGX SPARK BY 2X FOR THE SAME $4,000 00:11 four Arc Pro B70s racked in one chassis. 32GB of GDDR6 each, $949 MSRP, roughly $1,000 street per card stack four and you get 128GB of VRAM for about $4,000 total. a lab benchmarked the quad against a DGX Spark and a single RTX Pro 6000. the four B70s doubled the Spark or better they trail the Pro 6000, but that card is $10,000. so the real choice is now $4K for 128GB from Intel or $10K for 96GB from NVIDIA the article covers the DGX Spark at $3,999 as the single-desk local AI ceiling. this Intel quad matches the price, beats the memory throughput, and nobody is talking about it yet save this before Intel's VRAM-per-dollar advantage becomes the default homelab build ↓

INTEL JUST QUIETLY BUILT THE CHEAPEST PATH TO 128GB OF LOCAL AI VRAM, AND IT BEATS NVIDIA'S DGX SPARK BY 2X FOR THE SAME $4,000 00:11 four Arc Pro B70s racked in one chassis. 32GB of GDDR6 each, $949 MSRP, roughly $1,000 street per card stack four and you get 128GB of VRAM for about $4,000 total. a lab benchmarked the quad against a DGX Spark and a single RTX Pro 6000. the four B70s doubled the Spark or better they trail the Pro 6000, but that card is $10,000. so the real choice is now $4K for 128GB from Intel or $10K for 96GB from NVIDIA the article covers the DGX Spark at $3,999 as the single-desk local AI ceiling. this Intel quad matches the price, beats the memory throughput, and nobody is talking about it yet save this before Intel's VRAM-per-dollar advantage becomes the default homelab build ↓

NO1ennn

139,901 views • 2 months ago

Nemotron-3-Nano-Omni-30B-A3B smashing 80 concurrent sessions on a single NVIDIA AI DGX Spark! 🔥 256k context. MTP enabled. All sessions running completely different prompts. ~800 tokens/s cumulative 🤯 Video starts slow but goes hard — watch till the end!

Nemotron-3-Nano-Omni-30B-A3B smashing 80 concurrent sessions on a single NVIDIA AI DGX Spark! 🔥 256k context. MTP enabled. All sessions running completely different prompts. ~800 tokens/s cumulative 🤯 Video starts slow but goes hard — watch till the end!

Mia

22,853 views • 2 months ago

✨ One year after the debut of "Project Digits," DGX Spark has transformed from concept to reality and is now available from NVIDIA and OEM partners. See it in action lighting up the show floor at #CES2026. #SparkSomethingBig

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NVIDIA AI Developer

38,253 views • 8 months ago

For the innovator who has everything (except this) . . . 🎁 Compact, capable, and efficient -- NVIDIA DGX Spark is the perfect companion for anyone ready to build, prototype, or refine their AI ideas. Give the gift that doesn’t just keep on giving -- it keeps innovating.

For the innovator who has everything (except this) . . . 🎁 Compact, capable, and efficient -- NVIDIA DGX Spark is the perfect companion for anyone ready to build, prototype, or refine their AI ideas. Give the gift that doesn’t just keep on giving -- it keeps innovating.

NVIDIA

27,226 views • 9 months ago

The Hermes Agent Accelerated Business Hackathon presented by NVIDIA AI × Stripe × Nous Research starts now, for builders making agents that can earn, spend, and run real operations at any scale. Our NVIDIA integrations let your team run agents safely through NemoClaw, quickly on Nemotron 3 Ultra, and intelligently with access to their extensive agent skills. The new Stripe Skills for Hermes let your agent buy what it needs, provision its own SaaS, and pay for the services it uses. We want to see what kind of business tooling you can build on top of this foundation, whether it’s a fully automated company or a framework to accelerate enterprise functions. Prizes: 1st — $10,000 cash + NVIDIA DGX Spark + $5,000 Stripe Credits 2nd — $5,000 cash + NVIDIA DGX Spark + $3,000 Stripe Credits 3rd — $2,500 cash + NVIDIA DGX Spark + $1,000 Stripe Credits To enter: 1) Tweet a 1-3 minute demo video tagging Nous Research with a short writeup 2) Drop the link in the submissions channel: 3) Fill out the submission form: Judged by Nous Research, NVIDIA, and Stripe on usefulness, viability, and presentation. Submissions due EOD Tuesday, June 30.

The Hermes Agent Accelerated Business Hackathon presented by NVIDIA AI × Stripe × Nous Research starts now, for builders making agents that can earn, spend, and run real operations at any scale. Our NVIDIA integrations let your team run agents safely through NemoClaw, quickly on Nemotron 3 Ultra, and intelligently with access to their extensive agent skills. The new Stripe Skills for Hermes let your agent buy what it needs, provision its own SaaS, and pay for the services it uses. We want to see what kind of business tooling you can build on top of this foundation, whether it’s a fully automated company or a framework to accelerate enterprise functions. Prizes: 1st — $10,000 cash + NVIDIA DGX Spark + $5,000 Stripe Credits 2nd — $5,000 cash + NVIDIA DGX Spark + $3,000 Stripe Credits 3rd — $2,500 cash + NVIDIA DGX Spark + $1,000 Stripe Credits To enter: 1) Tweet a 1-3 minute demo video tagging Nous Research with a short writeup 2) Drop the link in the submissions channel: 3) Fill out the submission form: Judged by Nous Research, NVIDIA, and Stripe on usefulness, viability, and presentation. Submissions due EOD Tuesday, June 30.

Nous Research

1,891,649 views • 3 months ago

This is where it all started. 🚀 Ten years ago at #NVIDIAGTC, the journey of #NVIDIADGX began with our CEO Jensen Huang's announcement of DGX-1, the world’s first AI supercomputer. What started as a single system has since evolved into the universal blueprint for modern AI factories. Relive this momentous beginning and explore where the next decade of AI discovery will take us. Add DGX sessions to your GTC schedule now: #DecadeOfDGX

This is where it all started. 🚀 Ten years ago at #NVIDIAGTC, the journey of #NVIDIADGX began with our CEO Jensen Huang's announcement of DGX-1, the world’s first AI supercomputer. What started as a single system has since evolved into the universal blueprint for modern AI factories. Relive this momentous beginning and explore where the next decade of AI discovery will take us. Add DGX sessions to your GTC schedule now: #DecadeOfDGX

NVIDIA

34,003 views • 6 months ago

This - in my eyes - is the future of rendering: A coding model (LLM) creates a rough outline of the game with simple geometry but without shading. Then a video AI model generates the final beautiful image. Meet "Code World Model":

This - in my eyes - is the future of rendering: A coding model (LLM) creates a rough outline of the game with simple geometry but without shading. Then a video AI model generates the final beautiful image. Meet "Code World Model":

Mark Kretschmann

18,478 views • 20 days ago

CHINESE DEVELOPER JUST DROPPED A FULL 8-MINUTE SETUP GUIDE FOR THE NVIDIA DGX SPARK - AND SHOWED EXACTLY HOW TO RUN AI AGENTS AND ROBOTICS SIMULATIONS ON IT he unboxed the DGX Spark on camera - plugged in power, keyboard and ethernet and was inside the system within minutes then showed how to install iSECSIM 5.1 for virtual simulations, cloned the repo from GitHub, configured GCC 11 and built the entire stack from scratch 128GB of available memory, ARM processor, full Python library support and a complete AI development environment - all on a box the size of a paperback book at the end he launched a robotics environment and showed the DGX Spark handling full physics simulations in real time from unboxing to running agents in under 8 minutes - full guide on camera

CHINESE DEVELOPER JUST DROPPED A FULL 8-MINUTE SETUP GUIDE FOR THE NVIDIA DGX SPARK - AND SHOWED EXACTLY HOW TO RUN AI AGENTS AND ROBOTICS SIMULATIONS ON IT he unboxed the DGX Spark on camera - plugged in power, keyboard and ethernet and was inside the system within minutes then showed how to install iSECSIM 5.1 for virtual simulations, cloned the repo from GitHub, configured GCC 11 and built the entire stack from scratch 128GB of available memory, ARM processor, full Python library support and a complete AI development environment - all on a box the size of a paperback book at the end he launched a robotics environment and showed the DGX Spark handling full physics simulations in real time from unboxing to running agents in under 8 minutes - full guide on camera

Sprytix

57,512 views • 3 months ago

watch and let this sit for a second anon, a 35B AI model, building a complete multi-file game by itself on hermes agent. no cloud or api. not one line of code written by a human. running on a DGX spark in 2026. because not long ago this was science fiction. this is Ornith, a new open agentic coding model, running near lossless on a single DGX Spark, a 128GB box that sits on your desk. i hand it one spec and step back. it reasons about the architecture, creates the project structure, writes every module one by one, and here's the part that got me, it checks its own work as it goes. makes a call, reads the result, decides if it's right, corrects itself before moving on. no steering from me. this is what people keep underestimating about local AI. they hear "local" and think weaker, slower, a toy. watch the clip. a 35B at near full precision, planning and self correcting on a single desktop. the open models got genuinely good at agentic work, not benchmark good, actually builds the thing good. sped 5x so you can watch the whole build start to finish. this is where local AI actually is now. not coming soon. here.

watch and let this sit for a second anon, a 35B AI model, building a complete multi-file game by itself on hermes agent. no cloud or api. not one line of code written by a human. running on a DGX spark in 2026. because not long ago this was science fiction. this is Ornith, a new open agentic coding model, running near lossless on a single DGX Spark, a 128GB box that sits on your desk. i hand it one spec and step back. it reasons about the architecture, creates the project structure, writes every module one by one, and here's the part that got me, it checks its own work as it goes. makes a call, reads the result, decides if it's right, corrects itself before moving on. no steering from me. this is what people keep underestimating about local AI. they hear "local" and think weaker, slower, a toy. watch the clip. a 35B at near full precision, planning and self correcting on a single desktop. the open models got genuinely good at agentic work, not benchmark good, actually builds the thing good. sped 5x so you can watch the whole build start to finish. this is where local AI actually is now. not coming soon. here.

Sudo su

30,299 views • 2 months ago

watch this anon. i gave NVIDIA's biggest model ever a single task. 100 minutes and 440,000 tokens later, it had rendered nothing. not one important thing on the screen. this is Nemotron 3 Ultra. 550 billion parameters, a hybrid Mamba Transformer MoE, the largest model NVIDIA has ever shipped, and they built it specifically for long-running agentic coding. so i handed it exactly that: build a 3D scene from a spec, multiple files, iterate until the tests pass. the same task a frontier model one shotted in minutes. i genuinely wanted to be impressed. it ran for an hour and forty. burned through 440,000 tokens. wrote every file, passed its own tests, and proudly printed "task complete."the browser was blank. the 3D scene never rendered. not once. and the long horizon agentic behavior was genuinely good. it stayed on task the whole hour and forty, wrote real multi-file code, drove its own tools without derailing. it just couldn't turn any of that into something that actually runs. here's the part that gets me. it's a text model, it cannot see its own output. so it sat there looping on a broken vision tool, trying to "look" at the page, hitting error after error, never once reasoning its way out. it declared victory on an empty screen because it had no way to know the screen was empty. to be fair, i genuinely don't know what quant the NIM was serving, so maybe some of that's on the serving, not the model. but the biggest model NVIDIA has ever made, on the exact task it was designed for, couldn't tell it had built nothing in 100 minutes. same task on a local model, below thread👇.

watch this anon. i gave NVIDIA's biggest model ever a single task. 100 minutes and 440,000 tokens later, it had rendered nothing. not one important thing on the screen. this is Nemotron 3 Ultra. 550 billion parameters, a hybrid Mamba Transformer MoE, the largest model NVIDIA has ever shipped, and they built it specifically for long-running agentic coding. so i handed it exactly that: build a 3D scene from a spec, multiple files, iterate until the tests pass. the same task a frontier model one shotted in minutes. i genuinely wanted to be impressed. it ran for an hour and forty. burned through 440,000 tokens. wrote every file, passed its own tests, and proudly printed "task complete."the browser was blank. the 3D scene never rendered. not once. and the long horizon agentic behavior was genuinely good. it stayed on task the whole hour and forty, wrote real multi-file code, drove its own tools without derailing. it just couldn't turn any of that into something that actually runs. here's the part that gets me. it's a text model, it cannot see its own output. so it sat there looping on a broken vision tool, trying to "look" at the page, hitting error after error, never once reasoning its way out. it declared victory on an empty screen because it had no way to know the screen was empty. to be fair, i genuinely don't know what quant the NIM was serving, so maybe some of that's on the serving, not the model. but the biggest model NVIDIA has ever made, on the exact task it was designed for, couldn't tell it had built nothing in 100 minutes. same task on a local model, below thread👇.

Sudo su

32,589 views • 2 months ago

I think I have made the coolest rig for an home AI setup. - 4× DGX Spark + OEM PSUs - 1× 8-port multi-gig switch - 8× short Cat6 - 1× vertical PDU / quality power strip - 3D printed chassis (and other small components + accessories) At the moment it is just a virtual replica. But if NVIDIA AI Developer wants to sponsor me with a real DGX Spark I am open to discuss

I think I have made the coolest rig for an home AI setup. - 4× DGX Spark + OEM PSUs - 1× 8-port multi-gig switch - 8× short Cat6 - 1× vertical PDU / quality power strip - 3D printed chassis (and other small components + accessories) At the moment it is just a virtual replica. But if NVIDIA AI Developer wants to sponsor me with a real DGX Spark I am open to discuss

Marco Franzon

44,590 views • 1 month ago

WeiboAI dropped VibeThinker-3B, so I had to try it locally. this is a 3B model, not a giant frontier system. in the video I load it on my DGX Spark, give it 3 small algorithm problems, stream the actual model output live, then run the generated python through pytest. no benchmark screenshot no canned answer just a tiny local reasoner writing code and real tests deciding if it worked it went 3/3.

WeiboAI dropped VibeThinker-3B, so I had to try it locally. this is a 3B model, not a giant frontier system. in the video I load it on my DGX Spark, give it 3 small algorithm problems, stream the actual model output live, then run the generated python through pytest. no benchmark screenshot no canned answer just a tiny local reasoner writing code and real tests deciding if it worked it went 3/3.

Joey

16,865 views • 3 months ago

Fully local Code Assistant running on NVIDIA GPU! In this tutorial, I'll show you how to run Llama3 using TensorRT and Nvidia's Triton Inference Server to use it as a Code Assistant in VSCode In this thread 🧵, I'll walk you through the integration process, explaining each step simply and clearly 👇 NVIDIA AI NVIDIA AIDev AI at Meta

Fully local Code Assistant running on NVIDIA GPU! In this tutorial, I'll show you how to run Llama3 using TensorRT and Nvidia's Triton Inference Server to use it as a Code Assistant in VSCode In this thread 🧵, I'll walk you through the integration process, explaining each step simply and clearly 👇 NVIDIA AI NVIDIA AIDev AI at Meta

Daniel San

42,154 views • 2 years ago