3.3M
SHThe NVIDIA RTX 50 series GPUs have been revealed starting at $1,109 AUD for the 5070!
#NVIDIARTX50 #RTX5070 #RTX5080 #RTX5090 #NVIDIARTX5080 #GPU #CES2025 @nvidia.ai.pc
@shannongrixti
#Nvidia Gpus
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299.5K
NIRating Nvidia GPUs
@niktekofficial
733.9K
RYWhat is your favorite GPU?
#pcgaming #pcbuild #pcmasterrace #rtx5070 #rtx4070 #rx9070xt #rx9070
@ryzentek
6.1K
RA#GPU #tier #list #nvidia #amd#tier #categories #of #nvidia #GPU #RTX #Rx #GPU #nvidia #graphic #processing #unit #nvidiageforce #Nvidia #NvidiaGPU #GeForce #GeForceRTX #RTX #RTXOn #NvidiaGeForce #RTXGaming #RTXPerformance #NvidiaRTX #NvidiaLovers #NvidiaTechnology #NvidiaBuilds #RTXPower #RayTracing #DLSS #NvidiaFans #GTX #NvidiaInnovation #NvidiaGraphics
@ravikanth.kondapalli
12.4M
KOThe GTX 1080 Ti isn’t just a graphics card — it’s a moment in PC gaming history.
When it launched, it wasn’t supposed to exist. It was too powerful for the price, too close to Titan performance, and so far ahead of everything else that it reset expectations overnight. NVIDIA accidentally dropped a card that made “flagship” actually mean something.
11GB of VRAM.
Massive memory bandwidth.
No artificial segmentation.
For years, it handled everything you threw at it. 1080p, 1440p, early 4K — no excuses, no compromises. Even as newer generations launched, the 1080 Ti refused to feel obsolete. People upgraded around it instead of replacing it.
What made it special wasn’t just raw performance — it was value, longevity, and trust. You bought a 1080 Ti and knew you were set for years. No gimmicks. No “wait for the Super version.” No paying extra just to unlock features.
And that’s why people still love it.
Not because it beats modern GPUs in benchmarks — it doesn’t.
But because it represents a time when hardware felt honest, complete, and worth the money.
The GTX 1080 Ti didn’t age gracefully — it aged legendary.
#gpu #nvidia #gamingpc #graphicscard #1080ti
@koukanuk
4.2M
THNvidia DGX GPU💀 >>>
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NVIDIA DGX is a series of workstations and servers that use graphics processing units (GPUs) for deep learning applications. DGX systems are designed to enhance the performance of neural networks by using general-purpose computing on GPUs.
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#nvidia #nvidiadjx #nvidiagpu #jensenhuang #gpu #viral #phonk #phonkedits #troll #tech #edits #edit
@theartificialintelligence
3.9M
KARank GPU #pc #pcmasterrace #pcgamers #gamingpc #gpu #graphicscard #fyp #foryou
@kaduhtech
299.9K
NVGet an inside look at NVIDIA Blackwell GPUs and the latest data center technologies that are powering the new era of computing and #generativeAI. #GTC24
Visit our Blackwell highlight to watch the entire video.
@nvidia
880.2K
TEFor years, Nvidia owned 100% of the AI compute market. Every startup, every cloud, every lab ran on green.
But the leak of Google’s “Ironwood” chip changes everything. It’s a specialized processor that strips away everything unnecessary for AI.
Nvidia GPUs: Versatile, expensive, power-hungry.
Google TPUs: Specialized, efficient, purpose-built.
Google just used these chips to build Gemini 3, beating GPT-5 benchmarks without touching a single Nvidia GPU. The hardware war has officially begun.
@tenbolabs
1.2M
NONVIDIA DGX™ Spark: The Desktop Supercomputer Built for Real AI Work 🚀🤖
NVIDIA unveiled the DGX™ Spark at GTC, and it’s basically a high-end AI rig built for developers who need serious power on-site. Jensen Huang called it a “small supercomputer,” and that’s pretty much what it is.
You can train big models, tune them, and test them without waiting on cloud credits or long queues. The new NVIDIA GPUs push heavy workloads fast, so projects in deep learning, computer vision, and language modeling run smooth.
It also ships with NVIDIA AI Enterprise, TensorRT, and RAPIDS, so you get a clean pipeline from prototype to deployment without fighting your hardware.
If you’re building real AI systems and want your compute in the same room you work in, this machine makes sense. It’s built for speed, scale, and control.
Would you plug this into your workflow? Tell me below. ⬇️
Follow @calminmotivation for more 🔥
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#nvidia #usa #ai #tech #developer #jensenhuang
@noblessence_
802.3K
LENow you know how to use 2 different gpu’s in a single pc for extra performance. #gpu #nvidia #amd #losslesscaling #gaming
@leonistech
8.6M
HAThis customer wanted a maxed-out AI-ready PC, and we delivered with dual GPUs for maximum performance. 💪
A lot of you asked, “What’s the point of two GPUs?” Well, here’s why it makes a huge difference:
AI & Machine Learning: Two GPUs drastically reduce training time and improve parallel processing, perfect for deep learning and LLMs.
Multiple Monitors & Workflows: Ideal for setups with 3+ monitors, especially for productivity heavy environments like trading, content creation, or data science.
Rendering, Mining & Simulations: Whether you’re doing GPU-based rendering, crypto mining, or real time simulations, more GPU power means better efficiency.
Workload Distribution: Split heavy tasks across GPUs to keep your system cool and responsive.
It’s a serious tool for serious computing, in other words, it’s a flex:)
SPECS:
CPU: @amd Ryzen 9 9950X3D
MB: @asusrog X870E ROG Crosshair Hero
RAM: @gskillgaming 128GB DDR5
Storage: 4TB @samsungus 9100 Pro
GPU 1: @rog_usa ASUS ROG RTX 5090 Astral
GPU 2: ASUS ROG RTX 5090 Astral
AIO: @tryxglobal Panorama SE 360mm
PSU: 2500w Hela
Fans: 4x140mm @lianliofficial TL LCD + 3x120mm Lian Li TL fans
Case: @phanteks NV9
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#techbaddie #halloftech #fyp #tech #pcsetup #setup #gamimg #gamingpc #rog #pc #ai #highend #5090 #reels
@hall_of_tech
✨ Guida alla Scoperta #Nvidia Gpus
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