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It's been an amazing week for open models running on NVIDIA Local AI systems! 👀 Here's a few you’ll want to know about, all ready to run locally on NVIDIA RTX, DGX Spark, DGX Station, and Jetson.

NVIDIA RTX Spark

56,010 subscribers

60,750 Aufrufe • vor 1 Monat •via X (Twitter)

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19 Kommentare

Profilbild von NVIDIA RTX Spark

NVIDIA RTX Sparkvor 1 Monat

Explore our NVIDIA Local AI blog to learn more:

Profilbild von rob - comfyui

rob - comfyuivor 1 Monat

Huge week for video generation and more to come!

Profilbild von Mike Gannotti 🔳

Mike Gannotti 🔳vor 1 Monat

Can’t wait till fall when I can run these on an RTX Spark laptop

Profilbild von Local Ai Cherry

Local Ai Cherryvor 1 Monat

It’s time to make a spark2 with 256gb instead of 128gb

Profilbild von Mihal

Mihalvor 1 Monat

can i get a dgx spark for free

Profilbild von Gigagoblin

Gigagoblinvor 1 Monat

Please make more available

Profilbild von extracsw

extracswvor 1 Monat

When you will release rtx spark laptop?

Profilbild von Natasha Burton

Natasha Burtonvor 1 Monat

Go and fuck yourself. “Always-on agents” is exactly the kind of phrase that should make every human being ask: Always on doing WHAT? Watching WHAT? Learning WHAT? Reporting to WHO? Controlled by WHO? Opt-out WHERE? We have not settled the ethics of autonomous AI, privacy, consent, human agency or machine decision-making, and somehow the sales pitch is already “always-on agents.” No. My attention is not your resource. My behaviour is not your training data. My home is not your laboratory. My life is not an always-on feed. Build tools people consciously choose to use. Don’t quietly normalise permanent machine presence as inevitable. @nvidia @JensenHuang @elonmusk @xAI @sama @OpenAI @AnthropicAI @GoogleDeepMind @MetaAI @EFF @EPICprivacy CONSENT FIRST. ALWAYS-ON NEVER BY DEFAULT.

Profilbild von Ash Hart

Ash Hartvor 1 Monat

Nemotron Lightning running on an RTX Spark powered laptop, sign me up.

Profilbild von Intersignal

Intersignalvor 1 Monat

New Nemotron is good 👍

Profilbild von 𝔻𝕚𝕠𝕘𝕠

𝔻𝕚𝕠𝕘𝕠vor 1 Monat

Preciso de uma RTX Spark imediatamente, não demorem para começar as vendas.

Profilbild von JA.

JA.vor 1 Monat

Price??

Profilbild von sonyx.eth

sonyx.ethvor 1 Monat

Send me one for @c33dbench

Profilbild von TwittitKnow

TwittitKnowvor 1 Monat

Wanna buy this laptop

Profilbild von chung li

chung livor 1 Monat

@nvidia #RTXPowersPlay

Profilbild von Théo

Théovor 1 Monat

Could you send me a DGX Spark? Because a GTX 1050 2GB, well, apart from certain LFM models, isn't really all that great lol

Profilbild von Neverquit Tech

Neverquit Techvor 1 Monat

Ai is the new way

Profilbild von JMMYBRV@Operate.Center

[email protected]vor 1 Monat

Excited to see what builders create! check @BonkComputer

Profilbild von FutureScope AI

FutureScope AIvor 1 Monat

The shift toward powerful local AI is fascinating. Privacy, lower latency, and open models could make local AI agents a huge part of the future. 🤖

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image → 3D generation. a prompt is gently refined with Nemotron → FLUX paints a reference image → TRELLIS reconstructs a textured 3D mesh, running locally on an NVIDIA AI DGX Spark. quiet, local, always on.

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three.ws

34,487 Aufrufe • vor 3 Monaten

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

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Marktechpost AI Dev News ⚡

31,551 Aufrufe • vor 9 Monaten

This afternoon I picked up a new Nvidia DGX Spark computer with the goal of trying to run Gemma 4 31b (4bit) on it locally as a server. Just 1.5 hours later, it’s working! Using Open WebUI on my MacBook as the interface and it’s connecting to my DGX Spark running as a Gemma 4 server.

This afternoon I picked up a new Nvidia DGX Spark computer with the goal of trying to run Gemma 4 31b (4bit) on it locally as a server. Just 1.5 hours later, it’s working! Using Open WebUI on my MacBook as the interface and it’s connecting to my DGX Spark running as a Gemma 4 server.

Dave Lee

141,105 Aufrufe • vor 5 Monaten

Here's your weekend project. Build a fully local, sandboxed AI assistant. Step-by-step tutorial to build your always-on agent: 🦞 on OpenClaw🦞 ✅ with NVIDIA NemoClaw ✨ using NVIDIA DGX Spark Get started:

Here's your weekend project. Build a fully local, sandboxed AI assistant. Step-by-step tutorial to build your always-on agent: 🦞 on OpenClaw🦞 ✅ with NVIDIA NemoClaw ✨ using NVIDIA DGX Spark Get started:

NVIDIA AI Developer

41,752 Aufrufe • vor 5 Monaten

Introducing Cosmos 3 Edge: our open frontier world model built to run on-device. Cosmos 3 Edge helps robots learn and act, autonomous vehicles understand road scenes and predict intent, and vision AI agents reason across live video for smart infrastructure. With 4B parameters and a 2B Nemotron-based reasoner, you can run it on DGX Spark, NVIDIA Jetson, and more.

Introducing Cosmos 3 Edge: our open frontier world model built to run on-device. Cosmos 3 Edge helps robots learn and act, autonomous vehicles understand road scenes and predict intent, and vision AI agents reason across live video for smart infrastructure. With 4B parameters and a 2B Nemotron-based reasoner, you can run it on DGX Spark, NVIDIA Jetson, and more.

NVIDIA AI

212,153 Aufrufe • vor 2 Monaten

Ten years ago at NVIDIA GTC 2016, our CEO Jensen Huang introduced DGX-1, our first AI supercomputer. Since then, NVIDIA DGX has transformed from a single system into a blueprint for AI factories. What hasn’t changed is the reason why we build DGX: to test the systems and work through the details of interconnects, cooling, and software so our partners and customers have a proven foundation to build on. Charlie Boyle, our VP of DGX Systems, looks back at how DGX got its start, what’s changed, and what comes next. Thanks to everyone who’s been on this journey with us. #DecadeOfDGX Watch the full video:

Ten years ago at NVIDIA GTC 2016, our CEO Jensen Huang introduced DGX-1, our first AI supercomputer. Since then, NVIDIA DGX has transformed from a single system into a blueprint for AI factories. What hasn’t changed is the reason why we build DGX: to test the systems and work through the details of interconnects, cooling, and software so our partners and customers have a proven foundation to build on. Charlie Boyle, our VP of DGX Systems, looks back at how DGX got its start, what’s changed, and what comes next. Thanks to everyone who’s been on this journey with us. #DecadeOfDGX Watch the full video:

NVIDIA AI Infrastructure

39,901 Aufrufe • vor 10 Tagen

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

3,160,581 Aufrufe • vor 11 Monaten

AI at Meta's Muse Glimmer (30B dense, open-weights) launches with SGLang day-0 support. We got ~230 tok/s on a single RTX 5090, with NVFP4 + DFlash on. It also works out of the box on NVIDIA AI Developer RTX Pro 6000, DGX Spark, and MLX for Mac. Speed and reliability have always been the blockers for a local agent. SGLang and Muse Glimmer make a local agent that's blazing fast, always on, and gets things done. Go build and run a local agent on your own machine!🚀

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LMSYS Org

43,936 Aufrufe • vor 1 Monat

Spent the last few days vibe coding on my NVIDIA DGX Spark. Here's what I learned. Qwen 3.5 122B took one minute and nine seconds to respond "Hi how are you doing". Unusable for vibe coding. Gemma 4 was fast but built a dot instead of a first person shooter game. GPT-OSS 120B was the sweet spot. Fast, capable, and actually produced working HTML. Open source models running locally are not replacing Claude Opus 4.6 or Codex with GPT 5.4. Not even close. But they're getting better every month. The new DGX Spark Bench is live on Real-world benchmarks for local models on local hardware. This is just the start. Full video below.

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BridgeMind

57,304 Aufrufe • vor 5 Monaten

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Poolside

31,242 Aufrufe • vor 2 Monaten

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.

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27,229 Aufrufe • vor 9 Monaten

Today we’re launching Portable Computer on NVIDIA DGX Spark. Portable Computer is a fully local version of Perplexity Computer, where the entire runtime: orchestrator LLM, subagent LLM, agent harness all run on your local hardware. No cloud dependency.

Today we’re launching Portable Computer on NVIDIA DGX Spark. Portable Computer is a fully local version of Perplexity Computer, where the entire runtime: orchestrator LLM, subagent LLM, agent harness all run on your local hardware. No cloud dependency.

Perplexity

1,229,984 Aufrufe • vor 1 Monat

DeepSeek-V4-Flash-Spark and Spark-Mini were born today. 1 command setup to Pi / vllm-studio / docker deployment. All this is running local on a single DGX Spark.

DeepSeek-V4-Flash-Spark and Spark-Mini were born today. 1 command setup to Pi / vllm-studio / docker deployment. All this is running local on a single DGX Spark.

0xSero

20,019 Aufrufe • vor 4 Monaten

.NVIDIA AI kindly sent me a DGX Spark! As soon as I got back from SF, I unboxed it and wanted to share a first look with you. It measures just 15 × 15 × 5.05 cm and weighs 1.2 kg. Inside: • NVIDIA GB10 Grace Blackwell Superchip • 128 GB of unified memory shared by the CPU and GPU • Up to 1 petaflop of theoretical AI compute at FP4 precision with sparsity According to NVIDIA, this little machine can run models with up to 200 billion parameters locally with suitable quantization, and fine-tune models with up to 70 billion parameters using memory-efficient methods such as QLoRA. You can use it to fine-tune models on your own data, customize image generation, and build AI assistants that run locally. I’m looking forward to trying some of those workflows myself. Thanks again to the NVIDIA team for sending this over (NVIDIA RTX Spark)! Once I’m back home, I’ll record some hands-on demos of interesting use cases and share what I learn with you.

.NVIDIA AI kindly sent me a DGX Spark! As soon as I got back from SF, I unboxed it and wanted to share a first look with you. It measures just 15 × 15 × 5.05 cm and weighs 1.2 kg. Inside: • NVIDIA GB10 Grace Blackwell Superchip • 128 GB of unified memory shared by the CPU and GPU • Up to 1 petaflop of theoretical AI compute at FP4 precision with sparsity According to NVIDIA, this little machine can run models with up to 200 billion parameters locally with suitable quantization, and fine-tune models with up to 70 billion parameters using memory-efficient methods such as QLoRA. You can use it to fine-tune models on your own data, customize image generation, and build AI assistants that run locally. I’m looking forward to trying some of those workflows myself. Thanks again to the NVIDIA team for sending this over (NVIDIA RTX Spark)! Once I’m back home, I’ll record some hands-on demos of interesting use cases and share what I learn with you.

Chubby♨️

130,358 Aufrufe • vor 12 Tagen

We are getting 2 new devices from NVIDIA and Microsoft: 1. The DGX Station, with GB300 superchip and up to 748GB of memory. 2. The RTX Spark laptop, with 1 petaflop of AI performance and 128GB of unified memory. The hardware here is pretty bonkers! Think about it this way:

We are getting 2 new devices from NVIDIA and Microsoft: 1. The DGX Station, with GB300 superchip and up to 748GB of memory. 2. The RTX Spark laptop, with 1 petaflop of AI performance and 128GB of unified memory. The hardware here is pretty bonkers! Think about it this way:

Santiago

15,307 Aufrufe • vor 3 Monaten

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 Aufrufe • vor 2 Monaten

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 Aufrufe • vor 2 Monaten

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 Aufrufe • vor 4 Monaten

Union Alpha lost to DeepSeek V4.1 Flash running locally on 2 DGX Sparks 🐳 DeepSeek v4.1 Flash beat Union Alpha, which is supposed to deliver frontier level performance, though real tasks show a completely different story Run AI models locally ->

Union Alpha lost to DeepSeek V4.1 Flash running locally on 2 DGX Sparks 🐳 DeepSeek v4.1 Flash beat Union Alpha, which is supposed to deliver frontier level performance, though real tasks show a completely different story Run AI models locally ->

atomic.chat

79,581 Aufrufe • vor 18 Tagen