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We built a Local AI Hardware Arena using ODS LLM races between - RTX PRO 6000 - DGX Spark - Strix Halo - M5 MacBook Pro - The Cloud (ChatGPT) Let us know what you wanna see next We will make Local AI The Default

Ahmad

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17,288 次观看 • 21 天前 •via X (Twitter)

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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 次观看 • 8 个月前

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

139,651 次观看 • 1 个月前

Introducing local Reachy Mini conversations: free chats forever! So fast that we had to hardcode delays to stop it from interrupting you mid-sentence. We built an open-source Realtime API powered by llama.cpp: Parakeet -> Gemma 4 E4B -> Qwen3TTS Run it anywhere you run local LMs. Video shows DGX Spark and a 36GB M3 Pro MacBook. Blog:

Introducing local Reachy Mini conversations: free chats forever! So fast that we had to hardcode delays to stop it from interrupting you mid-sentence. We built an open-source Realtime API powered by llama.cpp: Parakeet -> Gemma 4 E4B -> Qwen3TTS Run it anywhere you run local LMs. Video shows DGX Spark and a 36GB M3 Pro MacBook. Blog:

Andi Marafioti

76,922 次观看 • 2 个月前

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

321,624 次观看 • 3 个月前

NVIDIA CEO JENSEN HUANG: THE AI APOCALYPSE NARRATIVE IS COMPLETE NONSENSE "it's computer and software. we make it better every year because we know exactly how it works" the real disruption is developers deploying local setups to out-produce entire teams: > hardware scale - from a $249 Jetson Orin Nano Super to a $2,999 DGX Spark with 128GB of unified memory > local inference - DGX Spark runs 70B-200B models on 1 PFLOP of throughput with zero cloud GPU rental costs > database privacy - redirecting Python API clients to Ollama takes one line of code to bypass subscription bills the people who configure local AI today are going to look very far ahead of the curve by 2027 read the setup guide below 👇

NVIDIA CEO JENSEN HUANG: THE AI APOCALYPSE NARRATIVE IS COMPLETE NONSENSE "it's computer and software. we make it better every year because we know exactly how it works" the real disruption is developers deploying local setups to out-produce entire teams: > hardware scale - from a $249 Jetson Orin Nano Super to a $2,999 DGX Spark with 128GB of unified memory > local inference - DGX Spark runs 70B-200B models on 1 PFLOP of throughput with zero cloud GPU rental costs > database privacy - redirecting Python API clients to Ollama takes one line of code to bypass subscription bills the people who configure local AI today are going to look very far ahead of the curve by 2027 read the setup guide below 👇

Mr. Buzzoni

15,201 次观看 • 2 个月前

Qwen 3.6 35B running at over 100 tokens per second on my $5,399 MacBook Pro M5 Max. This is the best local AI model I have ever run. 128GB of unified memory. No cloud. No API costs. No rate limits. Just raw local inference at speeds I didn't think were possible on a laptop. This model is more intelligent than GPT 5 on benchmarks. Running locally. On a MacBook. For free after the hardware cost. I said local AI would never compete with frontier. I'm starting to rethink that. The gap is closing faster than anyone expected.

Qwen 3.6 35B running at over 100 tokens per second on my $5,399 MacBook Pro M5 Max. This is the best local AI model I have ever run. 128GB of unified memory. No cloud. No API costs. No rate limits. Just raw local inference at speeds I didn't think were possible on a laptop. This model is more intelligent than GPT 5 on benchmarks. Running locally. On a MacBook. For free after the hardware cost. I said local AI would never compete with frontier. I'm starting to rethink that. The gap is closing faster than anyone expected.

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The new DGrid AI Arena is here🏆 We just dropped a quick video walkthrough to help you get started in minutes. Come join the arena — we’d love to see you battle, build, and explore with us!🕹️ #DGrid #AIArena #AI #LLM

The new DGrid AI Arena is here🏆 We just dropped a quick video walkthrough to help you get started in minutes. Come join the arena — we’d love to see you battle, build, and explore with us!🕹️ #DGrid #AIArena #AI #LLM

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Can a $351 mini PC run a 30B+ AI model locally? 👀 We tested the MINISFORUM UM790 Pro with Qwen 35B — and the results might surprise you. 💻 Powered by: • AMD Ryzen™ 9 7940HS • Radeon™ 780M iGPU • 48GB DDR5 RAM (dual-channel) 🚀 Smooth local inference at 20+ tokens/sec (based on our test setup, results may vary) No cloud. No API fees. Just local AI. 🔒 🔗 Learn more: #MINISFORUM #UM790Pro #LocalAI #MiniPC #AI #LLM #Tech

MINISFORUM Official

74,557 次观看 • 4 个月前

I'm working with Exo to make local AI viable. I will make sure that: 1. I perfect the recipe for making the best LLM compressions w REAP and 4bit quantisation 2. I will make finding, hosting, optimising and USING LLMs so easy you won't know the difference. Open source must win

I'm working with Exo to make local AI viable. I will make sure that: 1. I perfect the recipe for making the best LLM compressions w REAP and 4bit quantisation 2. I will make finding, hosting, optimising and USING LLMs so easy you won't know the difference. Open source must win

0xSero

11,881 次观看 • 3 个月前

YOUR OLD ANDROID PHONE IS A FREE AI VISION SERVER AND YOU DON’T KNOW IT one dev built an app that turns any android into a local openai-compatible LLM server with vision support, analyzes camera feeds entirely on-device, no cloud, no subscriptions, nothing leaves your network you already own the hardware, it’s just sitting in a drawer full breakdown below ↓

YOUR OLD ANDROID PHONE IS A FREE AI VISION SERVER AND YOU DON’T KNOW IT one dev built an app that turns any android into a local openai-compatible LLM server with vision support, analyzes camera feeds entirely on-device, no cloud, no subscriptions, nothing leaves your network you already own the hardware, it’s just sitting in a drawer full breakdown below ↓

leopardracer

21,844 次观看 • 2 个月前

💥AI is no longer confined to the cloud. At Computex, Jack Huynh showcased the AMD Ryzen AI Halo and Ryzen AI Max PRO 400 Series processors. The next wave of innovation will be built, trained, and run locally. As AI evolves from chatbots to intelligent agents, developers and enterprises need the performance, memory capacity, and flexibility to bring these experiences to life directly on the device. ⚡ Ryzen AI Halo enables local AI development with support for models up to 200B parameters, up to 128GB of unified memory, ROCm optimization, and Windows + Linux support. ⚡ Ryzen AI Max PRO 400 Series combines AI, graphics, and compute in a single architecture, delivering workstation-class performance for next-gen Agent Computers and AI PCs. Some of the most impactful AI experiences will happen right at your desk, where latency, privacy, and control matter most. We're just getting started.

💥AI is no longer confined to the cloud. At Computex, Jack Huynh showcased the AMD Ryzen AI Halo and Ryzen AI Max PRO 400 Series processors. The next wave of innovation will be built, trained, and run locally. As AI evolves from chatbots to intelligent agents, developers and enterprises need the performance, memory capacity, and flexibility to bring these experiences to life directly on the device. ⚡ Ryzen AI Halo enables local AI development with support for models up to 200B parameters, up to 128GB of unified memory, ROCm optimization, and Windows + Linux support. ⚡ Ryzen AI Max PRO 400 Series combines AI, graphics, and compute in a single architecture, delivering workstation-class performance for next-gen Agent Computers and AI PCs. Some of the most impactful AI experiences will happen right at your desk, where latency, privacy, and control matter most. We're just getting started.

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We ran the latest AI models from leading Chinese labs through some 3D builds and game-clone generations in Code Arena for Lunar New Year 🧧 Take a look at what they created, and let us know what you think.

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A 24-YEAR-OLD CHINESE DEVELOPER FROM HANGZHOU TURNED RTX 4090 / 3090-CLASS GPU RACKS INTO HIS OWN PRIVATE AI CLOUD. HIS $740/MONTH AI BILL DROPPED TO $31 IN ELECTRICITY he got tired of paying for chatgpt, claude, cursor, openai api credits and every “pro” tool that quietly turns into another monthly tax. long context runs, codebase scans, document parsing, agent loops. every workflow ended with a new invoice so he built a local llm rack instead. used server hardware, RTX 4090 / 3090-class GPU boxes, ollama for automation, lm studio for testing models, llama.cpp for heavier local runs. around $6,200 upfront, but after that the cost is mostly power and maintenance now his scripts hit localhost instead of a cloud api. code reviews, private docs, chinese contracts, sql cleanup, support replies and research tasks stay inside the room. no token panic, no rate-limit wall, no sensitive files leaving his own machines the funny part is that he did not replace claude completely. he just stopped using frontier models for dumb volume work. 65% of daily ai tasks do not need the smartest model alive. they need cheap tokens, privacy and a machine that can run all night cloud ai is still the brain. local ai is the engine room. once he separated those two, his monthly ai stack stopped looking like subscriptions and started looking like infrastructure by 2027, owning your own local ai rack will not look extreme. it will look like the moment people realized renting intelligence forever was the expensive option.

A 24-YEAR-OLD CHINESE DEVELOPER FROM HANGZHOU TURNED RTX 4090 / 3090-CLASS GPU RACKS INTO HIS OWN PRIVATE AI CLOUD. HIS $740/MONTH AI BILL DROPPED TO $31 IN ELECTRICITY he got tired of paying for chatgpt, claude, cursor, openai api credits and every “pro” tool that quietly turns into another monthly tax. long context runs, codebase scans, document parsing, agent loops. every workflow ended with a new invoice so he built a local llm rack instead. used server hardware, RTX 4090 / 3090-class GPU boxes, ollama for automation, lm studio for testing models, llama.cpp for heavier local runs. around $6,200 upfront, but after that the cost is mostly power and maintenance now his scripts hit localhost instead of a cloud api. code reviews, private docs, chinese contracts, sql cleanup, support replies and research tasks stay inside the room. no token panic, no rate-limit wall, no sensitive files leaving his own machines the funny part is that he did not replace claude completely. he just stopped using frontier models for dumb volume work. 65% of daily ai tasks do not need the smartest model alive. they need cheap tokens, privacy and a machine that can run all night cloud ai is still the brain. local ai is the engine room. once he separated those two, his monthly ai stack stopped looking like subscriptions and started looking like infrastructure by 2027, owning your own local ai rack will not look extreme. it will look like the moment people realized renting intelligence forever was the expensive option.

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21,320 次观看 • 2 个月前

Local models aren’t fast and honestly they’re not cheaper (yet!) than your frontier model sub. BUT, you can burn infinite tokens 24/7– and that when things get interesting. That’s why Alex Finn runs a fleet of Mac Studios, a DGX Spark, and an RTX 5090 machine to power ambient AI across his business. In this ep, Alex shows me his local AI setup, including: - how he chooses his hardware - Tailscale to stay connected - how cc and local models work together - his software factory: from build loop to rocket emoji Plus, we have the honest OpenClaw vs Hermes debate 🦞 A big ty to our sponsors! Runway - The creative AI platform for images, video and more Atlassian Jira - Prioritize with insights, build with confidence Full episode:

Local models aren’t fast and honestly they’re not cheaper (yet!) than your frontier model sub. BUT, you can burn infinite tokens 24/7– and that when things get interesting. That’s why Alex Finn runs a fleet of Mac Studios, a DGX Spark, and an RTX 5090 machine to power ambient AI across his business. In this ep, Alex shows me his local AI setup, including: - how he chooses his hardware - Tailscale to stay connected - how cc and local models work together - his software factory: from build loop to rocket emoji Plus, we have the honest OpenClaw vs Hermes debate 🦞 A big ty to our sponsors! Runway - The creative AI platform for images, video and more Atlassian Jira - Prioritize with insights, build with confidence Full episode:

claire vo 🖤

54,333 次观看 • 1 个月前

it’s official, EYEKONS!! general tickets for the BEAUTIFUL CHAOS tour are on sale today 10am local time!! let us know which show we will see you at 🖤 tickets: #KATSEYE #KATSEYE_BEAUTIFULCHAOS #BEAUTIFULCHAOS

it’s official, EYEKONS!! general tickets for the BEAUTIFUL CHAOS tour are on sale today 10am local time!! let us know which show we will see you at 🖤 tickets: #KATSEYE #KATSEYE_BEAUTIFULCHAOS #BEAUTIFULCHAOS

KATSEYE

105,874 次观看 • 1 年前

THIS AI DEVELOPER RUNS 120B PARAMETER MODELS LOCALLY AND HIS MACBOOK PRO CAN’T EVEN OPEN THEM he bought a DGX Spark, gave it the model, went back to his desk by end of day the inference was done, tested, committed his team is still waiting for the cloud GPU queue to free up the only thing between them is 128GB of unified memory in a box smaller than your lunch one Spark is a datacenter node, two Sparks is your own cluster bookmark this and send it to whoever is still paying for cloud GPUs

THIS AI DEVELOPER RUNS 120B PARAMETER MODELS LOCALLY AND HIS MACBOOK PRO CAN’T EVEN OPEN THEM he bought a DGX Spark, gave it the model, went back to his desk by end of day the inference was done, tested, committed his team is still waiting for the cloud GPU queue to free up the only thing between them is 128GB of unified memory in a box smaller than your lunch one Spark is a datacenter node, two Sparks is your own cluster bookmark this and send it to whoever is still paying for cloud GPUs

leopardracer

85,726 次观看 • 2 个月前

GLM-5.2 running on Mac Studio Clusters or RTX GPU rigs isn't how Local AI will get mass adopted. For mass adoption we need to focus on two axes: 1. Intelligence/Speed Frontier. This is being tracked on local dot ai. Aggressive co-design is needed across the entire stack to push up the frontier specifically for local hardware. Better kernels, inference tricks like spec decoding, quantizing models, model routing. 2. Usability / UX. To use Local AI today, you need to install CLIs, select a model, a quantization, a harness that works best with that model, and you need to configure your inference engine. It's not very accessible. More work needs to be done on making this seamless, so the user doesn't even know the AI is running locally.

GLM-5.2 running on Mac Studio Clusters or RTX GPU rigs isn't how Local AI will get mass adopted. For mass adoption we need to focus on two axes: 1. Intelligence/Speed Frontier. This is being tracked on local dot ai. Aggressive co-design is needed across the entire stack to push up the frontier specifically for local hardware. Better kernels, inference tricks like spec decoding, quantizing models, model routing. 2. Usability / UX. To use Local AI today, you need to install CLIs, select a model, a quantization, a harness that works best with that model, and you need to configure your inference engine. It's not very accessible. More work needs to be done on making this seamless, so the user doesn't even know the AI is running locally.

Alex Cheema

30,800 次观看 • 1 个月前

Happily running Deepseek with unlimited tokens in OMP on 2 DGX Sparks. Yesterday I was explaining to someone about local ai and the power you have with it. He was so intrigued that you can run agents and workflows non stop and not pay anyone. That person was related to my boss. We will see what we build on prem.

Happily running Deepseek with unlimited tokens in OMP on 2 DGX Sparks. Yesterday I was explaining to someone about local ai and the power you have with it. He was so intrigued that you can run agents and workflows non stop and not pay anyone. That person was related to my boss. We will see what we build on prem.

Joey

16,779 次观看 • 10 天前

This guy plugged a DGX Spark (the $3K Nvidia box) and a Mac Mini M4 together to run AI and what happened next surprised everyone > the Nvidia box handles the hard part - processing your prompt in milliseconds > Mac Mini M4 handles the fast part - generating the response at memory bandwidth speeds nothing else can match > together they hit 84 tokens per second on Llama - 6x faster than the Spark alone > running compute agents locally on this setup means your data never leaves your hardware > two boxes. two different architectures. one AI system that Deepseek runs at data center scale he ran it on his desk save this. the way we build local AI is about to change

This guy plugged a DGX Spark (the $3K Nvidia box) and a Mac Mini M4 together to run AI and what happened next surprised everyone > the Nvidia box handles the hard part - processing your prompt in milliseconds > Mac Mini M4 handles the fast part - generating the response at memory bandwidth speeds nothing else can match > together they hit 84 tokens per second on Llama - 6x faster than the Spark alone > running compute agents locally on this setup means your data never leaves your hardware > two boxes. two different architectures. one AI system that Deepseek runs at data center scale he ran it on his desk save this. the way we build local AI is about to change

Mr. Buzzoni

153,768 次观看 • 2 个月前

Second, using 3.1 Flash-Lite in Google AI Studio, we built a retail business agent that plans and executes multi-step tasks for you like reporting and dashboard automation - saving you time to focus on the things that matter most. Beginning today, the model is rolling out in preview via the Gemini API in Google AI Studio and Vertex AI. Try it out and let us know what you think.

Second, using 3.1 Flash-Lite in Google AI Studio, we built a retail business agent that plans and executes multi-step tasks for you like reporting and dashboard automation - saving you time to focus on the things that matter most. Beginning today, the model is rolling out in preview via the Gemini API in Google AI Studio and Vertex AI. Try it out and let us know what you think.

Google AI

23,403 次观看 • 5 个月前