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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:

Theta Network

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19,321 просмотров • 3 месяцев назад •via X (Twitter)

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This is what happens when 48GB of VRAM isn't enough. Someone put 4× NVIDIA RTX A6000 inside an HP Z8 Fury G5. That gives you 192GB of VRAM across four GPUs. But here's the interesting part: You don't magically get one 192GB GPU. The memory is split across four cards, and the software has to manage how the model is distributed between them. That's why these machines are interesting for local AI. The problem isn't always compute. Sometimes you just need somewhere to put the model. Would you build this today, or go for a newer GPU with more bandwidth?

This is what happens when 48GB of VRAM isn't enough. Someone put 4× NVIDIA RTX A6000 inside an HP Z8 Fury G5. That gives you 192GB of VRAM across four GPUs. But here's the interesting part: You don't magically get one 192GB GPU. The memory is split across four cards, and the software has to manage how the model is distributed between them. That's why these machines are interesting for local AI. The problem isn't always compute. Sometimes you just need somewhere to put the model. Would you build this today, or go for a newer GPU with more bandwidth?

KyzoroX

81,436 просмотров • 24 дней назад

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

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

3,115,498 просмотров • 9 месяцев назад

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. 👇

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 просмотров • 2 лет назад

Some of the things you call ordinary today were once things you prayed for. Never forget to be grateful.

Some of the things you call ordinary today were once things you prayed for. Never forget to be grateful.

Power of Positivity

28,619 просмотров • 14 дней назад

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:

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

196,300 просмотров • 5 месяцев назад

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 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

18,707 просмотров • 3 месяцев назад

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

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

Nedbank

8,410,290 просмотров • 3 лет назад

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.

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 просмотров • 2 лет назад

One of the more insidious things is when men put down other women as a compliment. "You don't do x like them, that makes you better".

One of the more insidious things is when men put down other women as a compliment. "You don't do x like them, that makes you better".

AskAubry 🦋 🐆🦝

855,543 просмотров • 3 лет назад

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.

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

182,483 просмотров • 2 месяцев назад

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

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

Eva Angelina

15,133 просмотров • 6 месяцев назад

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.

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 просмотров • 1 год назад

🚨 YOUR GPU IS PROBABLY WASTING MORE THAN YOU THINK. vLLM is built to squeeze far more useful work out of your GPU when serving LLMs. Running an LLM at scale isn’t just about having a powerful GPU. The real problem is how efficiently you use its memory and compute. That’s where vLLM comes in. → High-throughput LLM inference and serving → PagedAttention for smarter KV-cache memory management → Continuous batching to keep GPUs busy → Prefix caching + chunked prefill → OpenAI-compatible API out of the box → Supports a huge range of modern LLM architectures → Quantization support for running models more efficiently And the crazy part? You can start an OpenAI-compatible inference server with: `vllm serve ` So your application can talk to your own model almost like it’s talking to OpenAI. The bigger idea: Don’t just buy more GPUs. Make the GPUs you already have work harder. That’s why vLLM has become such a major project in LLM inference. 🔥 #vLLM #AI #LLM #Inference #GPU #MachineLearning #AIInfrastructure #OpenSource #AIAgents #Developers

🚨 YOUR GPU IS PROBABLY WASTING MORE THAN YOU THINK. vLLM is built to squeeze far more useful work out of your GPU when serving LLMs. Running an LLM at scale isn’t just about having a powerful GPU. The real problem is how efficiently you use its memory and compute. That’s where vLLM comes in. → High-throughput LLM inference and serving → PagedAttention for smarter KV-cache memory management → Continuous batching to keep GPUs busy → Prefix caching + chunked prefill → OpenAI-compatible API out of the box → Supports a huge range of modern LLM architectures → Quantization support for running models more efficiently And the crazy part? You can start an OpenAI-compatible inference server with: `vllm serve ` So your application can talk to your own model almost like it’s talking to OpenAI. The bigger idea: Don’t just buy more GPUs. Make the GPUs you already have work harder. That’s why vLLM has become such a major project in LLM inference. 🔥 #vLLM #AI #LLM #Inference #GPU #MachineLearning #AIInfrastructure #OpenSource #AIAgents #Developers

Vikas gupta

14,138 просмотров • 18 дней назад

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.

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

28,353 просмотров • 1 год назад

compute is entering its price discovery era benchmarks and forward curves are turning gpu-hours into a hedgeable asset class as cme and silicon data prepare to introduce compute futures on october 5 incentiv is built for what happens when demand becomes economic state

compute is entering its price discovery era benchmarks and forward curves are turning gpu-hours into a hedgeable asset class as cme and silicon data prepare to introduce compute futures on october 5 incentiv is built for what happens when demand becomes economic state

Incentiv

10,716 просмотров • 1 месяц назад

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

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

HUEman ~ Yuri Mo

46,357 просмотров • 3 лет назад

“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

“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 просмотров • 8 месяцев назад