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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

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Ahmad

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17,288 Aufrufe • vor 27 Tagen •via X (Twitter)

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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 ↓
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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 ↓

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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:

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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

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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

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85,726 Aufrufe • vor 2 Monaten

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1:52

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.

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30,800 Aufrufe • vor 1 Monat

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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.

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16,779 Aufrufe • vor 16 Tagen

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
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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

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153,768 Aufrufe • vor 2 Monaten

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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.

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