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Some things don't need to be complicated. GPU compute is one of them. Pay only for what you use, nothing more. Find a GPU that works for you today:

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Theta Network

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18,947 görüntüleme • 1 ay önce •via X (Twitter)

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One of the things you learn when you use a wood stove is that you should NOT be using an axe to chop your wood. you should be using a maul. an axe is for felling trees. the pronounced wedge of the maul works to SPLIT, not cut, the wood
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One of the things you learn when you use a wood stove is that you should NOT be using an axe to chop your wood. you should be using a maul. an axe is for felling trees. the pronounced wedge of the maul works to SPLIT, not cut, the wood

Joseph Norman

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In what ways are you using #AI on your RTX GPU in your personal or professional life? 🤔 Reply with your answer and use #AIonRTX for a chance to win a MSI Gaming GeForce RTX 4080 GAMING X TRIO GPU. 👇
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In what ways are you using #AI on your RTX GPU in your personal or professional life? 🤔 Reply with your answer and use #AIonRTX for a chance to win a MSI Gaming GeForce RTX 4080 GAMING X TRIO GPU. 👇

NVIDIA AI Developer

49,468 görüntüleme • 2 yıl önce

one direction from this that excites me: a learning base instead of a storage one: not for what you already know, but for what you don't. made one for deep reading of plato's timaeus. 2 things i carried over: non-rag, indexed fs, and /raw-is-sacred to separate sources from generated content. a few features i find genuinely helpful:
0:12

one direction from this that excites me: a learning base instead of a storage one: not for what you already know, but for what you don't. made one for deep reading of plato's timaeus. 2 things i carried over: non-rag, indexed fs, and /raw-is-sacred to separate sources from generated content. a few features i find genuinely helpful:

Kat ⊷ the Poet Engineer

195,419 görüntüleme • 4 ay önce

ROBA Compute is our GPU execution layer for robotics. A distributed infrastructure layer for simulation, training, inference, and autonomous workflows — built for our stack first, then opened to external builders. GPU rental is already a category. The missing layer is robotics-native infrastructure: templates, allocation, benchmarking, and persistent execution for embodied AI. That’s the opportunity. ROBA Compute is designed to become the infrastructure layer where robotics workloads actually run.
0:14

ROBA Compute is our GPU execution layer for robotics. A distributed infrastructure layer for simulation, training, inference, and autonomous workflows — built for our stack first, then opened to external builders. GPU rental is already a category. The missing layer is robotics-native infrastructure: templates, allocation, benchmarking, and persistent execution for embodied AI. That’s the opportunity. ROBA Compute is designed to become the infrastructure layer where robotics workloads actually run.

Roba Labs

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Take control of your money. Transact better by paying only for what you use, and nothing more. #TakeYourMoneySeriously
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Take control of your money. Transact better by paying only for what you use, and nothing more. #TakeYourMoneySeriously

Nedbank

8,410,148 görüntüleme • 3 yıl önce

Sparse allocation/rendering of SDF bricks is now working! The pipeline is fully GPU-driven - a series of compute shaders that manages brick allocation/baking in a 3D texture atlas, followed by draw-indirect for rendering. Still a lot more work to do, including automatic LOD.
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Sparse allocation/rendering of SDF bricks is now working! The pipeline is fully GPU-driven - a series of compute shaders that manages brick allocation/baking in a 3D texture atlas, followed by draw-indirect for rendering. Still a lot more work to do, including automatic LOD.

Mike Turitzin

39,022 görüntüleme • 2 yıl önce

Free NVIDIA GPU with 16 GB VRAM GPU for Running Local LLMs! If you want to master local LLMs but you're waiting until you can afford a $1,500 GPU, you're honestly not going to make it. The open source AI ecosystem is moving way too fast for you to wait on your budget to catch up. Especially when you can build a bleeding edge inference engine from scratch right now, completely for free. You don't need a heavy local rig to start. Google is literally letting you use an enterprise grade NVIDIA Tesla T4 GPU for $0/hour. At standard cloud computing rates (~$0.20/hr), Google Colab’s 4 hour daily free tier hands you roughly $24 worth of data center tier GPU compute every single month. And most people just waste it. Let’s talk about the hardware you get access to for free. The NVIDIA Tesla T4 is an absolute workhorse: - Architecture: NVIDIA Turing (TU104) - VRAM: 16GB GDDR6 (320 GB/s bandwidth) - Compute: 320 Tensor Cores | 2560 CUDA Cores - Performance: 130 TOPS INT8 | 8.1 TFLOPS FP32 - Power: Sipping energy at a max 70W TDP This is the exact same hardware I used to run DeepMind's Gemma 4 26B A4B QAT MoE at a 250,000 context window without a single Out Of Memory (OOM) crash. If you have a web browser and 10 minutes, you have everything you need. I’ve put together a fully documented, cell by cell Google Colab notebook that teaches you exactly how to do this. Here is what the notebook actually teaches you: - How to provision an Ubuntu Linux environment with CUDA 13.0 and verify your driver stack. - How to pull the source code and compile the latest llama.cpp C++ binaries from scratch, specifically optimizing the build for your exact GPU using the -DCMAKE_CUDA_ARCHITECTURES=native flag. - How to directly download quantized local LLMs (GGUF format) straight from HuggingFace using the CLI. - How to manage 16GB VRAM limits, offload neural network layers to the GPU, and push massive context windows. Compile raw llama.cpp, ollama run a model, or spin up the LM Studio CLI. Pick whatever stack you are comfortable with. just start building. No hardware. No credit card. No excuses. Bookmark this post right now so you don't lose the tutorial. Even if you don't have time to run it today, you are going to want this workflow in your engineering toolkit. The link to the free Colab Notebook is in the comments below. Lemme know if you need more tutorials like this.
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Free NVIDIA GPU with 16 GB VRAM GPU for Running Local LLMs! If you want to master local LLMs but you're waiting until you can afford a $1,500 GPU, you're honestly not going to make it. The open source AI ecosystem is moving way too fast for you to wait on your budget to catch up. Especially when you can build a bleeding edge inference engine from scratch right now, completely for free. You don't need a heavy local rig to start. Google is literally letting you use an enterprise grade NVIDIA Tesla T4 GPU for $0/hour. At standard cloud computing rates (~$0.20/hr), Google Colab’s 4 hour daily free tier hands you roughly $24 worth of data center tier GPU compute every single month. And most people just waste it. Let’s talk about the hardware you get access to for free. The NVIDIA Tesla T4 is an absolute workhorse: - Architecture: NVIDIA Turing (TU104) - VRAM: 16GB GDDR6 (320 GB/s bandwidth) - Compute: 320 Tensor Cores | 2560 CUDA Cores - Performance: 130 TOPS INT8 | 8.1 TFLOPS FP32 - Power: Sipping energy at a max 70W TDP This is the exact same hardware I used to run DeepMind's Gemma 4 26B A4B QAT MoE at a 250,000 context window without a single Out Of Memory (OOM) crash. If you have a web browser and 10 minutes, you have everything you need. I’ve put together a fully documented, cell by cell Google Colab notebook that teaches you exactly how to do this. Here is what the notebook actually teaches you: - How to provision an Ubuntu Linux environment with CUDA 13.0 and verify your driver stack. - How to pull the source code and compile the latest llama.cpp C++ binaries from scratch, specifically optimizing the build for your exact GPU using the -DCMAKE_CUDA_ARCHITECTURES=native flag. - How to directly download quantized local LLMs (GGUF format) straight from HuggingFace using the CLI. - How to manage 16GB VRAM limits, offload neural network layers to the GPU, and push massive context windows. Compile raw llama.cpp, ollama run a model, or spin up the LM Studio CLI. Pick whatever stack you are comfortable with. just start building. No hardware. No credit card. No excuses. Bookmark this post right now so you don't lose the tutorial. Even if you don't have time to run it today, you are going to want this workflow in your engineering toolkit. The link to the free Colab Notebook is in the comments below. Lemme know if you need more tutorials like this.

Alok

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The new equation for AI infrastructure: “If power is the constraint and if compute is the asset, I think network is going to be the force multiplier.” Cisco President & CPO Jeetu Patel says every kilowatt you save moving the packet is a kilowatt you can give to the GPU. Jeetu Patel
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The new equation for AI infrastructure: “If power is the constraint and if compute is the asset, I think network is going to be the force multiplier.” Cisco President & CPO Jeetu Patel says every kilowatt you save moving the packet is a kilowatt you can give to the GPU. Jeetu Patel

a16z

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A lil throwback here for you… haha Sometimes you just have to make some adjustments when things get…. Complicated
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A lil throwback here for you… haha Sometimes you just have to make some adjustments when things get…. Complicated

Eva Angelina

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Color's got to be one of the fastest ways to recognize meaning. Green for done, red for priority, pink for insights.. It's like we're hard wired for it. However you use them, I'm hoping a color picker like this might make you want to use them more often.
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Color's got to be one of the fastest ways to recognize meaning. Green for done, red for priority, pink for insights.. It's like we're hard wired for it. However you use them, I'm hoping a color picker like this might make you want to use them more often.

Tobias Renström

46,912 görüntüleme • 11 ay önce

Recently implemented weapons. Feels so good to test this stuff. Why don't games have more simulated physics? It's so fun to play with! Compute shader is 3K lines already. 99% of game logic runs on GPU.
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Recently implemented weapons. Feels so good to test this stuff. Why don't games have more simulated physics? It's so fun to play with! Compute shader is 3K lines already. 99% of game logic runs on GPU.

Zolden

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Gm friends! Which flowers you prefer? Left or right? My GPU will need to render it few weeks for high resolution😬
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Gm friends! Which flowers you prefer? Left or right? My GPU will need to render it few weeks for high resolution😬

HUEman ~ Yuri Mo

46,353 görüntüleme • 3 yıl önce

I'M GIVING AWAY MNPCTECH GPU DISPLAY STAND! All you have to do Product link, To be eligible ✅Follow Mnpctech Audio Isolation Products. ✅ What is your Gpu model? ❤️Like 🔁Repost I’ll pick winner this Sunday!
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I'M GIVING AWAY MNPCTECH GPU DISPLAY STAND! All you have to do Product link, To be eligible ✅Follow Mnpctech Audio Isolation Products. ✅ What is your Gpu model? ❤️Like 🔁Repost I’ll pick winner this Sunday!

Mnpctech Audio Isolation Products.

12,301 görüntüleme • 2 yıl önce

“If you buy things for far below what they're worth, and you buy a group of them, you basically don't lose.” — Warren Buffett
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“If you buy things for far below what they're worth, and you buy a group of them, you basically don't lose.” — Warren Buffett

Investment Wisdom

65,954 görüntüleme • 7 ay önce

#RTXRemix can leverage AI on your RTX GPU to help remaster classic games! 🎮 What game would you like to see remastered with #RTXRemix? 🤔 Repost & tell us below with #AIonRTX for a chance to win a GeForce RTX 4090 GPU! 👇
0:17

#RTXRemix can leverage AI on your RTX GPU to help remaster classic games! 🎮 What game would you like to see remastered with #RTXRemix? 🤔 Repost & tell us below with #AIonRTX for a chance to win a GeForce RTX 4090 GPU! 👇

NVIDIA Studio

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Remember when Bill Gates said electric semi trucks would “probably never be a practical solution”? “The problem is that batteries are big and heavy. The more weight you’re trying to move, the more batteries you need to power the vehicle. But the more batteries you use, the more weight you add, and the more power you need. Even with big breakthroughs in battery technology, electric vehicles will probably never be a practical solution for things like 18-wheelers, cargo ships, and passenger jets. Electricity works when you need to cover short distances, but we need a different solution for heavy, long-haul vehicles.” Well… volume production of the Tesla Semi is beginning in 2026 with a target of 50,000 units per year.
0:10

Remember when Bill Gates said electric semi trucks would “probably never be a practical solution”? “The problem is that batteries are big and heavy. The more weight you’re trying to move, the more batteries you need to power the vehicle. But the more batteries you use, the more weight you add, and the more power you need. Even with big breakthroughs in battery technology, electric vehicles will probably never be a practical solution for things like 18-wheelers, cargo ships, and passenger jets. Electricity works when you need to cover short distances, but we need a different solution for heavy, long-haul vehicles.” Well… volume production of the Tesla Semi is beginning in 2026 with a target of 50,000 units per year.

Teslaconomics

80,864 görüntüleme • 4 ay önce

When you sell so many decades of your life for a salary that you spend your entire vacation sleeping Retirement at 65 is a scam. Use what they pay you to escape asap
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When you sell so many decades of your life for a salary that you spend your entire vacation sleeping Retirement at 65 is a scam. Use what they pay you to escape asap

Lazy Canadian Entrepreneur

12,404 görüntüleme • 1 yıl önce

🟢 News: GPU compute in the browser is finally real. WebGPU now ships by default in Chrome, Firefox, Safari, and Edge—not a polyfill, not behind a flag. You can run LLMs client-side. Transformers.js and ONNX Runtime already ship WebGPU backends. Eight years of spec work. No more asterisks. 🔗 #WebGPU #GPU #Chrome #Safari #Firefox #Edge
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🟢 News: GPU compute in the browser is finally real. WebGPU now ships by default in Chrome, Firefox, Safari, and Edge—not a polyfill, not behind a flag. You can run LLMs client-side. Transformers.js and ONNX Runtime already ship WebGPU backends. Eight years of spec work. No more asterisks. 🔗 #WebGPU #GPU #Chrome #Safari #Firefox #Edge

WebGL / WebGPU

12,788 görüntüleme • 6 ay önce