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⚡️ We just added support for Nvidia G4 (RTX Pro 6000 Blackwell Server Edition) GPUs in Colaboratory! 🔥 With a peak rating of 960 BF16 TFLOPs (~50% more than the A100-80G) and 96 GB of VRAM (20% more than the A100-80G), it’s the most efficient high-performance GPU we’ve ever... show more

Colaboratory

74,641 subscribers

63,096 次观看 • 8 个月前 •via X (Twitter)

教育科学技术

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245 tokens/sec on DeepSeek-V4-Flash and it’s only using 2 RTX 6000 Blackwell GPUs. tinycorp dropped a 2-GPU tinybox edition in honor of DeepSeek fully ready to expand to 4 GPUs later. The GPU middle class just got a serious upgrade.

245 tokens/sec on DeepSeek-V4-Flash and it’s only using 2 RTX 6000 Blackwell GPUs. tinycorp dropped a 2-GPU tinybox edition in honor of DeepSeek fully ready to expand to 4 GPUs later. The GPU middle class just got a serious upgrade.

Md Ismail Šojal 🕷️

16,148 次观看 • 3 个月前

Installing eight NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs into a single server. With 96GB of ECC GDDR7 memory per card, this system carries 768GB of total GPU memory, enough to run large language models, multi modal AI, and high throughput inference without spreading the work across multiple machines. Server Edition cards are passively cooled, with no fans of their own. They depend on the chassis to move air through them, which makes airflow planning and card placement critical in a dense eight GPU configuration. Every card is inspected, seated, and secured by hand, and the full system is validated under load before it ships. VRLA Tech Servers: #gpuserver #multigpu #aiworkflow #nvidia #pc

Installing eight NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs into a single server. With 96GB of ECC GDDR7 memory per card, this system carries 768GB of total GPU memory, enough to run large language models, multi modal AI, and high throughput inference without spreading the work across multiple machines. Server Edition cards are passively cooled, with no fans of their own. They depend on the chassis to move air through them, which makes airflow planning and card placement critical in a dense eight GPU configuration. Every card is inspected, seated, and secured by hand, and the full system is validated under load before it ships. VRLA Tech Servers: #gpuserver #multigpu #aiworkflow #nvidia #pc

VRLA Tech

1,100,000 次观看 • 25 天前

Amazon Web Services CEO Matt Garman said today there is so much demand they have NEVER retired an Nvidia A100 GPU. The A100 is nearly SIX YEARS OLD. And you're still bearish? "Because there is so much more demand than supply, there typically still is demand for the older chips .. we actually are completely sold out and have never retired an A100 server"

Amazon Web Services CEO Matt Garman said today there is so much demand they have NEVER retired an Nvidia A100 GPU. The A100 is nearly SIX YEARS OLD. And you're still bearish? "Because there is so much more demand than supply, there typically still is demand for the older chips .. we actually are completely sold out and have never retired an A100 server"

tae kim

145,227 次观看 • 9 个月前

Long-LRM can reconstruct large 3D scenes from up to 32 input images at 960x540 resolution in just 1.3 seconds on a single A100 80G GPU!

Long-LRM can reconstruct large 3D scenes from up to 32 input images at 960x540 resolution in just 1.3 seconds on a single A100 80G GPU!

Dreaming Tulpa 🥓👑

14,799 次观看 • 2 年前

NVIDIA GPU Price to Performance Tier List (2026) 🔥🎮 --------------- Thinking about buying a new GPU for gaming or PC build? Here’s a simple NVIDIA GPU tier list based on price-to-performance so you know which cards are worth it in 2026. 💻⚡ From RTX 5090 flagship power to budget GPUs like GTX 1650, this list helps you decide what to buy and what to avoid when building a gaming PC in Pakistan. 🇵🇰 ✅ Best GPUs for high-end gaming ✅ Value GPUs for 1080p & 1440p ❌ GPUs that are not worth buying anymore Save this before building your next gaming PC! 🚀 ---------------- KEYWORDS Nvidia GPU tier list 2026 Best Nvidia GPU for gaming RTX 5090 performance RTX 5080 gaming benchmarks RTX 5070 vs 4070 Best price to performance GPU Budget gaming GPU Pakistan Best RTX GPU for 1440p gaming Best graphics card for gaming PC Nvidia graphics card ranking Gaming GPU guide 2026 Best GPU for PC build -------------- HASHTAGS #pcgaming #gamingpc #pcbuild #nvidia #rtx #rtx5090 #rtx5080 #rtx5070 #rtx4060 #rtx4070 #graphicscard #gpu #gputierlist #gamingsetup #pcbuilder #pcmasterrace #gaminghardware #techreels #gamingpcbuild #pakistangaming #pcgamingpakistan #budgetpc #1440pgaming #4kgamingkanda

NVIDIA GPU Price to Performance Tier List (2026) 🔥🎮 --------------- Thinking about buying a new GPU for gaming or PC build? Here’s a simple NVIDIA GPU tier list based on price-to-performance so you know which cards are worth it in 2026. 💻⚡ From RTX 5090 flagship power to budget GPUs like GTX 1650, this list helps you decide what to buy and what to avoid when building a gaming PC in Pakistan. 🇵🇰 ✅ Best GPUs for high-end gaming ✅ Value GPUs for 1080p & 1440p ❌ GPUs that are not worth buying anymore Save this before building your next gaming PC! 🚀 ---------------- KEYWORDS Nvidia GPU tier list 2026 Best Nvidia GPU for gaming RTX 5090 performance RTX 5080 gaming benchmarks RTX 5070 vs 4070 Best price to performance GPU Budget gaming GPU Pakistan Best RTX GPU for 1440p gaming Best graphics card for gaming PC Nvidia graphics card ranking Gaming GPU guide 2026 Best GPU for PC build -------------- HASHTAGS #pcgaming #gamingpc #pcbuild #nvidia #rtx #rtx5090 #rtx5080 #rtx5070 #rtx4060 #rtx4070 #graphicscard #gpu #gputierlist #gamingsetup #pcbuilder #pcmasterrace #gaminghardware #techreels #gamingpcbuild #pakistangaming #pcgamingpakistan #budgetpc #1440pgaming #4kgamingkanda

Pc Guys

604,300 次观看 • 7 个月前

NVIDIA GPUs just got a BIG performance boost 🤯 Someone removed the hidden limits. > RTX 3060: +87% performance > RTX 3090: +65% > RTX 4090: Nearly 2x peak performance No BIOS flash. No driver patch

NVIDIA GPUs just got a BIG performance boost 🤯 Someone removed the hidden limits. > RTX 3060: +87% performance > RTX 3090: +65% > RTX 4090: Nearly 2x peak performance No BIOS flash. No driver patch

Zazzy Stardust

312,960 次观看 • 11 天前

Happy Friday! We just put DeepSeek-V4-Pro up on It’s the world’s largest open source model at 1.6T parameters, and you can run it for free running on NVIDIA Blackwell GPUs. Try the NVIDIA NIM API →

Happy Friday! We just put DeepSeek-V4-Pro up on It’s the world’s largest open source model at 1.6T parameters, and you can run it for free running on NVIDIA Blackwell GPUs. Try the NVIDIA NIM API →

NVIDIA AI

208,311 次观看 • 6 个月前

NVIDIA GPU Tier List 2026 | Best Graphics Cards Ranked by Price to Performance ------------------- This NVIDIA GPU tier list ranks modern graphics cards based on price-to-performance, comparing RTX and GTX series to highlight the best and worst options. Designed for gamers and PC enthusiasts, it helps you choose the right GPU for building or upgrading your system with maximum value. ------------------- Keywords: NVIDIA GPU tier list best graphics cards 2026 RTX GPU ranking price to performance GPU RTX 4090 vs RTX 4080 best GPU for gaming budget graphics cards NVIDIA RTX comparison GPU buying guide best PC build GPU RTX vs GTX performance latest NVIDIA GPUs ------------------- Hashtags: #NVIDIA #RTX4090 #RTX4080 #RTX5070 #GPUComparison #GPUTierList #GraphicsCard #PCGaming #GamingPC #PCBuild #TechGuide #Stationery #GamingSetup #socialmedia #Hardware #PCMasterRace #GamingPerformance #BuildAPC #TechReels

NVIDIA GPU Tier List 2026 | Best Graphics Cards Ranked by Price to Performance ------------------- This NVIDIA GPU tier list ranks modern graphics cards based on price-to-performance, comparing RTX and GTX series to highlight the best and worst options. Designed for gamers and PC enthusiasts, it helps you choose the right GPU for building or upgrading your system with maximum value. ------------------- Keywords: NVIDIA GPU tier list best graphics cards 2026 RTX GPU ranking price to performance GPU RTX 4090 vs RTX 4080 best GPU for gaming budget graphics cards NVIDIA RTX comparison GPU buying guide best PC build GPU RTX vs GTX performance latest NVIDIA GPUs ------------------- Hashtags: #NVIDIA #RTX4090 #RTX4080 #RTX5070 #GPUComparison #GPUTierList #GraphicsCard #PCGaming #GamingPC #PCBuild #TechGuide #Stationery #GamingSetup #socialmedia #Hardware #PCMasterRace #GamingPerformance #BuildAPC #TechReels

Zestro Gaming

468,300 次观看 • 7 个月前

🚨 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 次观看 • 2 个月前

All the Robotron you can eat at 10,000 frames per second on a 192-core Threadripper Dell 7875 with dual Blackwell RTX6000 GPUs and 196 GB of VRAM. Watch it live at

All the Robotron you can eat at 10,000 frames per second on a 192-core Threadripper Dell 7875 with dual Blackwell RTX6000 GPUs and 196 GB of VRAM. Watch it live at

Dave W Plummer

20,662 次观看 • 7 个月前

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 次观看 • 2 个月前

What’s in sarah guo's bag? The ultimate robotics brain. 🧠 NVIDIA Jetson AGX Thor, packing up to 2,070 FP4 TFLOPS of AI compute and 128 GB of memory. Power next generation humanoids and autonomous systems with real-time reasoning and server class performance, built for the lab, factory, and field. Learn more 👉

What’s in sarah guo's bag? The ultimate robotics brain. 🧠 NVIDIA Jetson AGX Thor, packing up to 2,070 FP4 TFLOPS of AI compute and 128 GB of memory. Power next generation humanoids and autonomous systems with real-time reasoning and server class performance, built for the lab, factory, and field. Learn more 👉

NVIDIA Robotics

93,861 次观看 • 3 个月前

Today NVIDIA revealed AI-powered DLSS 5 coming this year to the latest RTX GPUs, claiming "photorealism." What do you think of the results? Read more:

Today NVIDIA revealed AI-powered DLSS 5 coming this year to the latest RTX GPUs, claiming "photorealism." What do you think of the results? Read more:

80 LEVEL

11,939 次观看 • 8 个月前

72% of the intelligence with 3.8% of the GPUs. Just to show how talented Mistral AI team is: this shows the "intelligence per GPU" ratio. - Mistral 4: 3,800 Grace Blackwell (published) 🇫🇷 - GPT-6 Astra: 100,000+ Grace Blackwell (published) - Grok 4.7: ~200,000 GPUs (estimate) - Claude Opus 5.5: ~500,000 Trainium2 (estimate) Proud!

72% of the intelligence with 3.8% of the GPUs. Just to show how talented Mistral AI team is: this shows the "intelligence per GPU" ratio. - Mistral 4: 3,800 Grace Blackwell (published) 🇫🇷 - GPT-6 Astra: 100,000+ Grace Blackwell (published) - Grok 4.7: ~200,000 GPUs (estimate) - Claude Opus 5.5: ~500,000 Trainium2 (estimate) Proud!

Theo Otz

180,932 次观看 • 1 个月前

Today we’re launching Ornn Depreciation Curves, modeling GPU hardware values from A100 to GB300 using real transaction data. As new generations of GPUs enter the market, the future value of existing hardware becomes increasingly uncertain. Depreciation Curves provides a foundation for estimating GPU residual values, helping lenders and fleet operators better quantify the risks underlying financing and investment decisions. Explore the curves →

Today we’re launching Ornn Depreciation Curves, modeling GPU hardware values from A100 to GB300 using real transaction data. As new generations of GPUs enter the market, the future value of existing hardware becomes increasingly uncertain. Depreciation Curves provides a foundation for estimating GPU residual values, helping lenders and fleet operators better quantify the risks underlying financing and investment decisions. Explore the curves →

Ornn

23,739 次观看 • 3 天前

The "I don't have enough VRAM" excuse just died. I’m running Meta’s new 30B Muse Glimmer Q6_K_XL with a massive 130k context window on just 26GB VRAM FREE compute on Kaggle. Kaggle provides you free 2x Nvidia T4 GPUs. 30 hours usage each week! Yesterday, I showed you the violent throughput of Muse Glimmer on a single RTX 4090. Today, we are securing a Dual NVIDIA T4 GPU cluster with 32GB of total VRAM for exactly $0 and dropping the massive 24.5GB Q6_K_XL GGUF onto it. Here is the exact Kaggle workflow and benchmarking breakdown: # 1. The Storage Bypass & Setup I built a clean cell by cell script in the file. We dynamically fetch the CUDA accelerated llama.cpp binaries and use wget to stream the model directly into Kaggle's /kaggle/tmp scratch storage, which cleanly bypasses their 19.5GB output directory limit. # 2. The Multi GPU Performance With the -ngl 99 flag offloading all model layers across both T4 GPUs (32GB VRAM combined), we pushed a massive 131,072 token context window (-c 131072). The benchmark numbers: Prefill: 265.9 t/s Decode: 9.0 t/s VRAM Total: 26.5 GB # 3. The Architecture Insight The Q6_K_XL model itself is 24.5 GB. Because of Muse Glimmer's aggressive 16:1 GQA, the unquantized KV cache for a massive 130k context window only takes up 2 GB of memory. No heavily degraded Q4 KV quantization required. It just works. No compiling from source. No credit card. No OOM crashes. Zero excuses. If you’re running a single RTX 3090, 4090, or 5090, you need to experience this hyper efficient KV cache right now before the upcoming Qwen 3.8 27B drop completely steals your VRAM tomorrow. pick the Q4 or Q5 quants for 24 GB VRAM rigs. I'm dropping the Unsloth huggingface GGUF links and the free Kaggle notebook link in the replies. spin up your own instance, and show me your multi GPU benchmarks.

The "I don't have enough VRAM" excuse just died. I’m running Meta’s new 30B Muse Glimmer Q6_K_XL with a massive 130k context window on just 26GB VRAM FREE compute on Kaggle. Kaggle provides you free 2x Nvidia T4 GPUs. 30 hours usage each week! Yesterday, I showed you the violent throughput of Muse Glimmer on a single RTX 4090. Today, we are securing a Dual NVIDIA T4 GPU cluster with 32GB of total VRAM for exactly $0 and dropping the massive 24.5GB Q6_K_XL GGUF onto it. Here is the exact Kaggle workflow and benchmarking breakdown: # 1. The Storage Bypass & Setup I built a clean cell by cell script in the file. We dynamically fetch the CUDA accelerated llama.cpp binaries and use wget to stream the model directly into Kaggle's /kaggle/tmp scratch storage, which cleanly bypasses their 19.5GB output directory limit. # 2. The Multi GPU Performance With the -ngl 99 flag offloading all model layers across both T4 GPUs (32GB VRAM combined), we pushed a massive 131,072 token context window (-c 131072). The benchmark numbers: Prefill: 265.9 t/s Decode: 9.0 t/s VRAM Total: 26.5 GB # 3. The Architecture Insight The Q6_K_XL model itself is 24.5 GB. Because of Muse Glimmer's aggressive 16:1 GQA, the unquantized KV cache for a massive 130k context window only takes up 2 GB of memory. No heavily degraded Q4 KV quantization required. It just works. No compiling from source. No credit card. No OOM crashes. Zero excuses. If you’re running a single RTX 3090, 4090, or 5090, you need to experience this hyper efficient KV cache right now before the upcoming Qwen 3.8 27B drop completely steals your VRAM tomorrow. pick the Q4 or Q5 quants for 24 GB VRAM rigs. I'm dropping the Unsloth huggingface GGUF links and the free Kaggle notebook link in the replies. spin up your own instance, and show me your multi GPU benchmarks.

Alok

19,370 次观看 • 3 个月前

1 billion voxels in real-time : testing the upcoming EmberGen sparse solver with the #Nvidia RTX Pro 6000 and #DellProMax

1 billion voxels in real-time : testing the upcoming EmberGen sparse solver with the #Nvidia RTX Pro 6000 and #DellProMax

Jason Key

66,608 次观看 • 1 年前

> 8 GPUs in one server rig > dude went homeless to build it > electrical bill costs more than rent now > while everyone else pays $400/month to openai > a 2 GPU desktop kills the api bill forever > rtx 4080 super + rtx 5060 ti = 32gb vram > runs qwen 3.6 with 100k context locally > no rate limits, no api keys, no data leaving the room > agents loop 400 times for free > claude opus still wins on hard reasoning > but local handles 90% of daily work > $1,200 setup pays itself off in 4 months > bookmark this and read the article below

> 8 GPUs in one server rig > dude went homeless to build it > electrical bill costs more than rent now > while everyone else pays $400/month to openai > a 2 GPU desktop kills the api bill forever > rtx 4080 super + rtx 5060 ti = 32gb vram > runs qwen 3.6 with 100k context locally > no rate limits, no api keys, no data leaving the room > agents loop 400 times for free > claude opus still wins on hard reasoning > but local handles 90% of daily work > $1,200 setup pays itself off in 4 months > bookmark this and read the article below

starmex

167,058 次观看 • 5 个月前