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The exact NVIDIA optimization order I follow on every PC I optimize. Clean driver. GPU-Z check. NVIDIA App settings. Most people skip step two and wonder why their GPU is running slow. Full guide here.

Xilly

24,862 subscribers

23,658 次观看 • 2 个月前 •via X (Twitter)

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Xilly 的头像

Xilly2 个月前

NVIDIA PACK (DDU, NVCleanstall, GPU-Z):

Cryzy 的头像

Cryzy1 个月前

obs or nvidia app for clipping?

Styap | Twitch 的头像

Styap | Twitch1 个月前

I actually appreciate this informative format instead of the typical optimization slop I usually see on the TL.

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PC Users🚨 If you update your NVIDIA and AMD GPU Drivers via the Apps, you’re Killing your Gaming Performance on Windows This is the BEST way to Update/Reinstall Drivers to Improve FPS and Lower Latency Nvidia Control Panel Settings Included! PTU👇

PC Users🚨 If you update your NVIDIA and AMD GPU Drivers via the Apps, you’re Killing your Gaming Performance on Windows This is the BEST way to Update/Reinstall Drivers to Improve FPS and Lower Latency Nvidia Control Panel Settings Included! PTU👇

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I don't know what I expected but Linux gaming just works at this point. I'm on a fresh install of Fedora 44 on an NVIDIA GPU and everything just works. What's going on? Is 2026 really the year of Linux?

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72,922 次观看 • 4 个月前

YOUR PARENTS PAID FOR THE CUDA MOAT! The #1 contributor to the CUDA MOAT isn't the the developers at NVIDIA, but it is the millions of developers outside of NVIDIA that invent new algorithms for CUDA like Flash Attention. For most of them, it started with an GeForce gaming GPU. NVIDIA is the only companies that has an reasonable good developer stack on consumer grade GPUs. As people grow up beyond playing CSGO & League of Legends & Minecraft, they either become anime weeaboos or they start programming on their existing computer with has an GeForce GPU

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25,230 次观看 • 5 个月前

Here is Ternary Bonsai 27B running an end-to-end agentic workflow locally with Hermes on an NVIDIA GeForce RTX 5090 GPU. The model reasons, calls tools, reads outputs, modifies files, and surfaces insights - all on consumer hardware, while all private files, intermediate states, and iterations remain local.

Here is Ternary Bonsai 27B running an end-to-end agentic workflow locally with Hermes on an NVIDIA GeForce RTX 5090 GPU. The model reasons, calls tools, reads outputs, modifies files, and surfaces insights - all on consumer hardware, while all private files, intermediate states, and iterations remain local.

PrismML

154,473 次观看 • 2 个月前

It's here: We just hit superhuman performance on AI kernel optimization! Real customer models & production settings. Not toy problems (what I typically see). This is the year that Claude writes its own kernels, Codex its own kernels, for every new GPU that it wants to run on -- something that takes months to port between GPU generations today. This has a massive impact to scaling intelligence. More compute means getting the next frontier model sooner.

It's here: We just hit superhuman performance on AI kernel optimization! Real customer models & production settings. Not toy problems (what I typically see). This is the year that Claude writes its own kernels, Codex its own kernels, for every new GPU that it wants to run on -- something that takes months to port between GPU generations today. This has a massive impact to scaling intelligence. More compute means getting the next frontier model sooner.

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243,952 次观看 • 6 个月前

NVIDIA CEO, Jensen Huang: "Nobody writes prompts anymore. The new job is building Loops and Graphs." In 50 minutes he explains why the engineers who stopped prompting are already years ahead, and what they're building instead. The missing piece most people skip: loops handle the work, graphs handle the loops. Watch it, then read the full guide on loops and graphs below.

NVIDIA CEO, Jensen Huang: "Nobody writes prompts anymore. The new job is building Loops and Graphs." In 50 minutes he explains why the engineers who stopped prompting are already years ahead, and what they're building instead. The missing piece most people skip: loops handle the work, graphs handle the loops. Watch it, then read the full guide on loops and graphs below.

Mahax

1,240,649 次观看 • 1 个月前

I won an RTX 5090 GPU at Mistral AI worldwide hackathon in Paris, and I have absolutely no idea on how to use it. Here's what I actually built: an image generation model fine-tuned to do technical analysis on trading charts. I trained it on an NVIDIA cloud GPU then back-tested it against Binance historical data. The model was profitable. The jury didn't quite catch the vibe. But apparently someone did because two weeks later, they reached out to tell me I'd won the NVIDIA track. Today, a €4,000 RTX Founders Edition showed up at my door. Absolutely gorgeous piece of hardware. One small problem: I use a MacBook. So if anyone has creative suggestions for what to do with a top-tier GPU and no desktop to put it in… I'm listening Huge thanks to NVIDIA, Mistral AI and Iterate team for appreciating my weird idea and an attempt to push tech to its boundaries

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52,518 次观看 • 5 个月前

llama3 8B (not quantized) running on an heterogeneous home cluster made of: - iPhone 15 Pro Max - iPad Pro (not sure which version XD) - MacBook Pro ( M1 Max ) - NVIDIA GeForce 3080 (not visible in video) - 2x NVIDIA Titan X Pascal Very soon also supporting Android (I *have* to also add my NVIDIA Shield GPU!!!!!). Single code base, single model format (reduced and optimally distributed to every node to save space). Everything (including iOS code) is open here ... it would be really nice, with the help of the community, taking this project to the next level in terms of optimization and support. My vision is about a distributed inference server that can run any model on any backend in any cluster topology - let's fight programmed obsolescence and democratize inference!

llama3 8B (not quantized) running on an heterogeneous home cluster made of: - iPhone 15 Pro Max - iPad Pro (not sure which version XD) - MacBook Pro ( M1 Max ) - NVIDIA GeForce 3080 (not visible in video) - 2x NVIDIA Titan X Pascal Very soon also supporting Android (I have to also add my NVIDIA Shield GPU!!!!!). Single code base, single model format (reduced and optimally distributed to every node to save space). Everything (including iOS code) is open here ... it would be really nice, with the help of the community, taking this project to the next level in terms of optimization and support. My vision is about a distributed inference server that can run any model on any backend in any cluster topology - let's fight programmed obsolescence and democratize inference!

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304,161 次观看 • 2 年前

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11,663 次观看 • 1 年前

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58,431 次观看 • 5 个月前

I sat down with NVIDIA Sam Stanwyck (Sam Stanwyck) at ISC to talk about one of the most misunderstood areas in computing: quantum. We discussed why NVIDIA is not building a quantum computer, but instead the infrastructure around it: GPUs, AI, CUDA-Q, quantum simulation, control systems, error correction, and the path toward hybrid CPU/GPU/QPU supercomputing. The key takeaway: Quantum will likely not arrive as a standalone magic machine. It will arrive as part of a much larger accelerated computing stack, combining classical supercomputers, AI, GPUs, and quantum processors to tackle problems in chemistry, materials science, energy, optimization, and fundamental science. Full episode with Sam Stanwyck is now live.

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Chubby♨️

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GeniusThinking

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NVIDIA DGX Spark is the most powerful personal AI supercomputer right now. I am running AI agents working for me 24/7 while keeping my data 100% private. In this video I cover the full setup, real benchmarks and tradeoffs. Here's the honest breakdown:

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90,288 次观看 • 1 年前

🚨 I warned everyone about $NVDA… and people were pissed. On November 19, 2024, I said the last two times this happened to $NVDA, it dumped 20%. I said, "Don’t shoot the messenger—I hope I’m wrong." That video got 108,000 views, and the comments were filled with people saying I was crazy. Now? Nvidia is down 20.9%. In this video, I break down: 📉 Why we knew this sell-off was coming 🔍 The key levels Nvidia MUST hold ⚠️ How low it could go if support breaks

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54,618 次观看 • 1 年前

We are excited to share a deeper look into the GPU Sharing Platform that will power AI applications on the InfraX V4 DApp. In this demo we showcase a GPU running and completing an image generation task. Our backend successfully leverages this GPU (and CPU) in real time to complete the job, earning the user money, and powering the infraX platform. Here’s how it works 👉 when you connect to the InfraX DApp, our platform automatically detects the GPU on your machine and immediately checks it eligibility and where it can be used. From there, each user dictates exactly how and when they want to contribute their GPU power by setting the days their PC is most available (throughout the night, for example). Additional, each user will be able to pause their GPU's connection to infraX whenever they need to use it locally, resuming with a just a single click. Once activated, your GPU seamlessly plugs into the InfraX network and begins fulfilling compute jobs, earning YOU money. Go on, take the $INFRA pill..

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infraX | $INFRA

30,615 次观看 • 1 年前

Coaching kickers is more than rolling out footballs and telling them to kick. After 15 years in the NFL and years studying biomechanics and mental performance, I still demonstrate what I teach and patiently help athletes understand every detail, in person or virtually. Here is a slow motion full step kick, regular speed, full step, one step a no step part two, and a slow motion static no step part one #kickincoach #kickincoach #kicker #footballkicker

Coaching kickers is more than rolling out footballs and telling them to kick. After 15 years in the NFL and years studying biomechanics and mental performance, I still demonstrate what I teach and patiently help athletes understand every detail, in person or virtually. Here is a slow motion full step kick, regular speed, full step, one step a no step part two, and a slow motion static no step part one #kickincoach #kickincoach #kicker #footballkicker

Shayne Graham

33,899 次观看 • 8 天前

Dylan Patel of SemiAnalysis says a worse GPU with better storage and memory now beats the best chip without them, so buying the newest GPU alone no longer wins inference. So, an AMD GPU with more memory can outperform Nvidia in some cases. "So what we have is we have over $80 million of compute, GPUs from Nvidia, AMD, TPUs from Google, Trainium from Amazon, and we run this benchmark constantly on the newest inference engine, newest drivers, newest PyTorch version, whatever it is." "Every day it runs on an automated CI, and we run it on all the latest Chinese models, from GLM, Zhipu, Moonshot, Kimi, Alibaba, all these models we run." "Initially, when we were benchmarking the difference between these chips and different engines, different schemes for parallelism, we were just running it fixed context length." "But now with Agent X, we've analyzed over $5 million worth of Claude Code traces. This is real production traffic that people have donated to us as well as internally generated. Now we know what the actual agent workload looks like." "And then as we implement that and run those benchmarks, it turns out yes, the chip you're using is very important, but now even more important is how are you handling this memory offload?" "And so while an Nvidia GPU is faster than an AMD GPU in most cases, because AMD GPUs have more memory, they actually end up outperforming in some cases." "Or you can have a worse GPU, but a much better storage solution, and now you can outperform what the best GPU can do without those solutions. So just buying the newest and latest GPU alone doesn't get you the best inference economics." "Actually, you need to layer in all these other innovations including storage and memory." [ Who's the top player on your chart? ] "That really is a difficult multivariable problem. And generally that means you need to have, yes, you need to have the best GPU, a GB300, but you also need to have the best storage solutions. And so I won't spoil who's the best right here, but I will say that storage solutions matter a lot and memory solutions matter a lot, as does your front-end networking. That matters a lot."

Dylan Patel of SemiAnalysis says a worse GPU with better storage and memory now beats the best chip without them, so buying the newest GPU alone no longer wins inference. So, an AMD GPU with more memory can outperform Nvidia in some cases. "So what we have is we have over $80 million of compute, GPUs from Nvidia, AMD, TPUs from Google, Trainium from Amazon, and we run this benchmark constantly on the newest inference engine, newest drivers, newest PyTorch version, whatever it is." "Every day it runs on an automated CI, and we run it on all the latest Chinese models, from GLM, Zhipu, Moonshot, Kimi, Alibaba, all these models we run." "Initially, when we were benchmarking the difference between these chips and different engines, different schemes for parallelism, we were just running it fixed context length." "But now with Agent X, we've analyzed over $5 million worth of Claude Code traces. This is real production traffic that people have donated to us as well as internally generated. Now we know what the actual agent workload looks like." "And then as we implement that and run those benchmarks, it turns out yes, the chip you're using is very important, but now even more important is how are you handling this memory offload?" "And so while an Nvidia GPU is faster than an AMD GPU in most cases, because AMD GPUs have more memory, they actually end up outperforming in some cases." "Or you can have a worse GPU, but a much better storage solution, and now you can outperform what the best GPU can do without those solutions. So just buying the newest and latest GPU alone doesn't get you the best inference economics." "Actually, you need to layer in all these other innovations including storage and memory." [ Who's the top player on your chart? ] "That really is a difficult multivariable problem. And generally that means you need to have, yes, you need to have the best GPU, a GB300, but you also need to have the best storage solutions. And so I won't spoil who's the best right here, but I will say that storage solutions matter a lot and memory solutions matter a lot, as does your front-end networking. That matters a lot."

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178,673 次观看 • 1 个月前