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Blender on the latest RTX 6000 ADA, Dell T2 Pro Max👌 Even heavy path-traced production scenes become almost real-time. #b3d #Nvidia #DellProMax

Andrew Price

157,879 subscribers

72,614 просмотров • 10 месяцев назад •via X (Twitter)

Наука и технологии #b3d #Nvidia #DellProMax

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Private livecam show

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Testing the DLSS build of Blender 5.1 with OmniStep 2.. Real-time path tracing! OmniStep 2 - Now on BlenderMarket / Gumroad! #b3d #blender #omnistep

Testing the DLSS build of Blender 5.1 with OmniStep 2.. Real-time path tracing! OmniStep 2 - Now on BlenderMarket / Gumroad! #b3d #blender #omnistep

Damjan Minovski

34,666 просмотров • 6 месяцев назад

City-on-Web: Real-time Neural Rendering of Large-scale Scenes on the Web paper page: enables real-time neural rendering of large-scale scenes over web using laptop GPUs. The result is tested on the NVIDIA RTX 3060 Laptop GPU with 1920x1080 resolution

City-on-Web: Real-time Neural Rendering of Large-scale Scenes on the Web paper page: enables real-time neural rendering of large-scale scenes over web using laptop GPUs. The result is tested on the NVIDIA RTX 3060 Laptop GPU with 1920x1080 resolution

AK

79,439 просмотров • 2 лет назад

99% of Pixar shots will now render entirely on NVIDIA RTX PRO 6000 GPUs. RenderMan XPU, is reshaping Pixar’s workflow—bigger scenes, faster rendering, and unprecedented freedom coming for Toy Story 5… and beyond. 🎬 Watch the story behind the tech:

99% of Pixar shots will now render entirely on NVIDIA RTX PRO 6000 GPUs. RenderMan XPU, is reshaping Pixar’s workflow—bigger scenes, faster rendering, and unprecedented freedom coming for Toy Story 5… and beyond. 🎬 Watch the story behind the tech:

NVIDIA AI

20,179 просмотров • 9 месяцев назад

Real-time Fire and Liquid Simulation 🤯 Achieved using NVIDIA RTX PRO 6000 with 96 GB of VRAM 🤯🤯 Only Nerds like us get why this awesome!! Anybody who worked with 3D softwares knows how hard is it to learn and work with particle simulations in Real-time. Thanks to Dell Technologies and JangaFX Software we saw this Demo using the amazing softwares of EmberGen and LiquiGen where newcomers can quickly learn and execute complex simulations in real-time. Best part is you can export these simulations and bring it to your 3D software of choice such as Unreal Engine or Blender 🔶 😎😎

Real-time Fire and Liquid Simulation 🤯 Achieved using NVIDIA RTX PRO 6000 with 96 GB of VRAM 🤯🤯 Only Nerds like us get why this awesome!! Anybody who worked with 3D softwares knows how hard is it to learn and work with particle simulations in Real-time. Thanks to Dell Technologies and JangaFX Software we saw this Demo using the amazing softwares of EmberGen and LiquiGen where newcomers can quickly learn and execute complex simulations in real-time. Best part is you can export these simulations and bring it to your 3D software of choice such as Unreal Engine or Blender 🔶 😎😎

Bad Decisions Studio

40,028 просмотров • 1 год назад

I recreated the caustics sequences in Blade Runner in Blender, using Shaders Plus' Real-Time Caustics, and each frame took only roughly 20s per scene to render on an RTX 3080! 🤠 You can check out Shaders Plus here! :) #blender3d #b3d

I recreated the caustics sequences in Blade Runner in Blender, using Shaders Plus' Real-Time Caustics, and each frame took only roughly 20s per scene to render on an RTX 3080! 🤠 You can check out Shaders Plus here! :) #blender3d #b3d

SMOUSE 🔸

62,427 просмотров • 1 год назад

GPU GIVEAWAY Buy a GPU × GTC 2026 = Give a GPU > RTX PRO 6000 Blackwell > 96GB VRAM • NVFP4 > ~$15K value > Brand new If this does well I’ll ask NVIDIA for more GPUs next time… maybe even DGX Sparks How to enter? Short clip here Full clip in the replies GO GO GO

Ahmad

36,697 просмотров • 6 месяцев назад

Surface Laptop Ultra is the closest Microsoft has ever come to building a no‑compromise performance machine, and the details are interesting to say the least. A full NVIDIA RTX Spark platform, up to 128GB of unified memory, a 15‑inch mini‑LED display hitting 2,000 nits, native Arm gaming progress, and a cooling system built for sustained heavy workloads. What Microsoft and NVIDIA are doing together here raises real questions about where Windows on Arm goes next and how this shifts expectations for pro‑grade laptops. #Surface (1/2)

Surface Laptop Ultra is the closest Microsoft has ever come to building a no‑compromise performance machine, and the details are interesting to say the least. A full NVIDIA RTX Spark platform, up to 128GB of unified memory, a 15‑inch mini‑LED display hitting 2,000 nits, native Arm gaming progress, and a cooling system built for sustained heavy workloads. What Microsoft and NVIDIA are doing together here raises real questions about where Windows on Arm goes next and how this shifts expectations for pro‑grade laptops. #Surface (1/2)

Windows Central

13,939 просмотров • 3 месяцев назад

MiniMax M2.7 is 230B params. Can you actually run it at home? I tested Unsloth's UD-IQ3_XXS (80GB) on 4 different rigs: 🟠 4x RTX 4090 (96GB): 71.52 tok/s, TTFT 1045ms 🟢 4x RTX 5090 (128GB): 120.54 tok/s, TTFT 725ms 🟡 1x RTX PRO 6000 (96GB): 118.74 tok/s, TTFT 765ms 🟣 DGX Spark (128GB) — 24.41 tok/s, TTFT 741ms Backend: llama.cpp. Context: 32k. Max tokens: 4096. I went with IQ3_XXS because it's the biggest quant that fits in 96GB VRAM while still leaving safe headroom for 32k context. Same quant across all four rigs, fairest comparison I could run. Now look at rough peak GPU power draw: 🟠 4x4090 → 1,800W peak (450W × 4) 🟢 4x5090 → 2,300W peak (575W × 4) 🟡 RTX PRO 6000 → 600W peak 🟣 DGX Spark → 240W peak (whole system) The RTX PRO 6000 is the quiet winner. One card, 96GB, matching a 4x5090 rig at roughly a quarter of the power and zero multi-GPU headaches. Best tokens-per-watt by a wide margin. DGX Spark is slow on generation but pulls the least power of any rig here, around 240W for the whole system. Prefill-friendly, memory-rich, wall-socket-friendly. And yes, plenty of people cap their cards. Even then, 4x 4090 or 4x 5090 still pulls well over 1,200W from the GPUs alone.

MiniMax M2.7 is 230B params. Can you actually run it at home? I tested Unsloth's UD-IQ3_XXS (80GB) on 4 different rigs: 🟠 4x RTX 4090 (96GB): 71.52 tok/s, TTFT 1045ms 🟢 4x RTX 5090 (128GB): 120.54 tok/s, TTFT 725ms 🟡 1x RTX PRO 6000 (96GB): 118.74 tok/s, TTFT 765ms 🟣 DGX Spark (128GB) — 24.41 tok/s, TTFT 741ms Backend: llama.cpp. Context: 32k. Max tokens: 4096. I went with IQ3_XXS because it's the biggest quant that fits in 96GB VRAM while still leaving safe headroom for 32k context. Same quant across all four rigs, fairest comparison I could run. Now look at rough peak GPU power draw: 🟠 4x4090 → 1,800W peak (450W × 4) 🟢 4x5090 → 2,300W peak (575W × 4) 🟡 RTX PRO 6000 → 600W peak 🟣 DGX Spark → 240W peak (whole system) The RTX PRO 6000 is the quiet winner. One card, 96GB, matching a 4x5090 rig at roughly a quarter of the power and zero multi-GPU headaches. Best tokens-per-watt by a wide margin. DGX Spark is slow on generation but pulls the least power of any rig here, around 240W for the whole system. Prefill-friendly, memory-rich, wall-socket-friendly. And yes, plenty of people cap their cards. Even then, 4x 4090 or 4x 5090 still pulls well over 1,200W from the GPUs alone.

stevibe

191,782 просмотров • 4 месяцев назад

INTEL JUST QUIETLY BUILT THE CHEAPEST PATH TO 128GB OF LOCAL AI VRAM, AND IT BEATS NVIDIA'S DGX SPARK BY 2X FOR THE SAME $4,000 00:11 four Arc Pro B70s racked in one chassis. 32GB of GDDR6 each, $949 MSRP, roughly $1,000 street per card stack four and you get 128GB of VRAM for about $4,000 total. a lab benchmarked the quad against a DGX Spark and a single RTX Pro 6000. the four B70s doubled the Spark or better they trail the Pro 6000, but that card is $10,000. so the real choice is now $4K for 128GB from Intel or $10K for 96GB from NVIDIA the article covers the DGX Spark at $3,999 as the single-desk local AI ceiling. this Intel quad matches the price, beats the memory throughput, and nobody is talking about it yet save this before Intel's VRAM-per-dollar advantage becomes the default homelab build ↓

INTEL JUST QUIETLY BUILT THE CHEAPEST PATH TO 128GB OF LOCAL AI VRAM, AND IT BEATS NVIDIA'S DGX SPARK BY 2X FOR THE SAME $4,000 00:11 four Arc Pro B70s racked in one chassis. 32GB of GDDR6 each, $949 MSRP, roughly $1,000 street per card stack four and you get 128GB of VRAM for about $4,000 total. a lab benchmarked the quad against a DGX Spark and a single RTX Pro 6000. the four B70s doubled the Spark or better they trail the Pro 6000, but that card is $10,000. so the real choice is now $4K for 128GB from Intel or $10K for 96GB from NVIDIA the article covers the DGX Spark at $3,999 as the single-desk local AI ceiling. this Intel quad matches the price, beats the memory throughput, and nobody is talking about it yet save this before Intel's VRAM-per-dollar advantage becomes the default homelab build ↓

NO1ennn

139,803 просмотров • 1 месяц назад

FOUR 600W GPUS. 384GB OF GDDR7. AND DELL MADE THE INSTALL LOOK EASIER THAN BUILDING A GAMING PC. this is a Dell PowerEdge XE7740 receiving four NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs. each card brings: 96GB GDDR7 ECC 24,064 CUDA cores 4 PFLOPS FP4 up to 600W but the most satisfying part is the cable management. every GPU power lead is already routed, labeled and held beside its assigned slot. drop in the card. lock the retention hardware. connect one 16-pin plug. no loose bundle hanging across the airflow path and no guessing which cable belongs where. the four-card build totals 384GB of GPU memory and 16 PFLOPS FP4. the 4U XE7740 supports up to eight of these GPUs, doubling that to 768GB and 32 PFLOPS FP4. honest line: the memory remains split between separate cards, and four GPUs can still pull up to 2.4kW. Dell made a rack-scale AI server this dense without turning the next GPU swap into cable surgery. bookmark & watch today ↓

FOUR 600W GPUS. 384GB OF GDDR7. AND DELL MADE THE INSTALL LOOK EASIER THAN BUILDING A GAMING PC. this is a Dell PowerEdge XE7740 receiving four NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs. each card brings: 96GB GDDR7 ECC 24,064 CUDA cores 4 PFLOPS FP4 up to 600W but the most satisfying part is the cable management. every GPU power lead is already routed, labeled and held beside its assigned slot. drop in the card. lock the retention hardware. connect one 16-pin plug. no loose bundle hanging across the airflow path and no guessing which cable belongs where. the four-card build totals 384GB of GPU memory and 16 PFLOPS FP4. the 4U XE7740 supports up to eight of these GPUs, doubling that to 768GB and 32 PFLOPS FP4. honest line: the memory remains split between separate cards, and four GPUs can still pull up to 2.4kW. Dell made a rack-scale AI server this dense without turning the next GPU swap into cable surgery. bookmark & watch today ↓

Grimmer

26,693 просмотров • 26 дней назад

In my last post, someone said the animations looked laggy 👀 Turns out… they were right but only on simulator. I’d been testing on Rork simulator + my own simulator the whole time → UI felt choppy. So I spent time optimizing animations with Opus 4.7. Then I built it on a real device (iPhone 17 Pro Max)… And damn, smooth af. Almost zero lag. Now the app is ~80% done. I’m already using it daily at the gym. Even had a few gym bros ask: “yo what app is that?” Feels like I’m already 50% there lol 🤟

In my last post, someone said the animations looked laggy 👀 Turns out… they were right but only on simulator. I’d been testing on Rork simulator + my own simulator the whole time → UI felt choppy. So I spent time optimizing animations with Opus 4.7. Then I built it on a real device (iPhone 17 Pro Max)… And damn, smooth af. Almost zero lag. Now the app is ~80% done. I’m already using it daily at the gym. Even had a few gym bros ask: “yo what app is that?” Feels like I’m already 50% there lol 🤟

Alex Nguyen

26,445 просмотров • 4 месяцев назад

🚨 SPACEX IS REPEATING NVIDIA’S PATTERN 1:1 This is Nvidia’s 1999 chart overlaid on SpaceX today. And the overlap is almost PERFECT. Every week, I come back expecting the pattern to finally break. It does NOT. SpaceX keeps following the same structure: → Same early dump → Same weak recovery → Same final capitulation → Same accumulation zone → Same explosive breakout Now look at what happened to Nvidia. Nvidia eventually bottomed after a brutal drawdown. Institutions accumulated near the lows. Then the real long-term rally began. SpaceX is now tracking almost the exact same path. If the pattern continues, $80 remains my main bottom target before the next major rally. I’m not saying SpaceX must repeat Nvidia tick for tick. But when two market structures track this closely for this long, ignoring it becomes dangerous. People will say this time is different. They always do. What are your thoughts on this? Follow and turn notifications on. I’ll post the exact moment I start buying.

🚨 SPACEX IS REPEATING NVIDIA’S PATTERN 1:1 This is Nvidia’s 1999 chart overlaid on SpaceX today. And the overlap is almost PERFECT. Every week, I come back expecting the pattern to finally break. It does NOT. SpaceX keeps following the same structure: → Same early dump → Same weak recovery → Same final capitulation → Same accumulation zone → Same explosive breakout Now look at what happened to Nvidia. Nvidia eventually bottomed after a brutal drawdown. Institutions accumulated near the lows. Then the real long-term rally began. SpaceX is now tracking almost the exact same path. If the pattern continues, $80 remains my main bottom target before the next major rally. I’m not saying SpaceX must repeat Nvidia tick for tick. But when two market structures track this closely for this long, ignoring it becomes dangerous. People will say this time is different. They always do. What are your thoughts on this? Follow and turn notifications on. I’ll post the exact moment I start buying.

Wimar.X

76,128 просмотров • 25 дней назад

From 2 second (faster on higher end GPUs) Gaussian splat generations, using Apple's "Sharp Monocular View Synthesis in Less Than a Second" code, running locally on my system. Imported into Octane Render 2026 which has fully path traced rendering of Gaussian splats. Stuff like this is why I've been so excited to see where Gaussian splatting research would go. Combined with a real-time Generative AI "renderer" this stuff could start to get quite insane. Combined with Dreams it would be even better ofc :D Code to the Apple code in the comments below.

From 2 second (faster on higher end GPUs) Gaussian splat generations, using Apple's "Sharp Monocular View Synthesis in Less Than a Second" code, running locally on my system. Imported into Octane Render 2026 which has fully path traced rendering of Gaussian splats. Stuff like this is why I've been so excited to see where Gaussian splatting research would go. Combined with a real-time Generative AI "renderer" this stuff could start to get quite insane. Combined with Dreams it would be even better ofc :D Code to the Apple code in the comments below.

Martin Nebelong

36,774 просмотров • 8 месяцев назад

AMD CEO LISA SU HELD A MINI PC ON STAGE THAT RUNS A 235B MODEL AND REPLACES YOUR $440/MONTH AI STACK amd's ryzen ai max+ 395 is the first x86 chip that runs a 200 billion parameter model on one piece of silicon. cpu and gpu share 128gb of unified memory, no separate graphics card needed the gmktec evo-x2 runs qwen3 235b fully, deepseek v3 comfortably and llama 3.3 70b with headroom. on linux you get 110gb of usable vram out of 128gb amd claimed the chip beat an nvidia rtx 5080 by more than 3x on deepseek r1 inference. a lunchbox sized pc outrunning a $1,000 discrete gpu on a real ai workload a heavy ai user pays $200 for claude code max, $200 for chatgpt pro, $20 for cursor and $20 for gemini. that's $5,280 a year and the box pays itself off in 9 to 10 months install ollama, pull the model, point claude code at localhost. same interface, nothing leaves the machine, nothing costs per request bookmark this and read the article below

AMD CEO LISA SU HELD A MINI PC ON STAGE THAT RUNS A 235B MODEL AND REPLACES YOUR $440/MONTH AI STACK amd's ryzen ai max+ 395 is the first x86 chip that runs a 200 billion parameter model on one piece of silicon. cpu and gpu share 128gb of unified memory, no separate graphics card needed the gmktec evo-x2 runs qwen3 235b fully, deepseek v3 comfortably and llama 3.3 70b with headroom. on linux you get 110gb of usable vram out of 128gb amd claimed the chip beat an nvidia rtx 5080 by more than 3x on deepseek r1 inference. a lunchbox sized pc outrunning a $1,000 discrete gpu on a real ai workload a heavy ai user pays $200 for claude code max, $200 for chatgpt pro, $20 for cursor and $20 for gemini. that's $5,280 a year and the box pays itself off in 9 to 10 months install ollama, pull the model, point claude code at localhost. same interface, nothing leaves the machine, nothing costs per request bookmark this and read the article below

starmex

980,829 просмотров • 2 месяцев назад

GLM 5.3 Flash running locally makes a Minecraft black hole mod We ran GLM 5.3 Flash Q4 locally on a rented 4x RTX PRO 6000 box and asked it to make a lightning gun mod for the real game using the Fabric API Output: -7.6M tokens -Time: ~9 hours -Decode: ~96 tok/s The mod adds a black hole rifle, when shot it spawns a black hole that pulls in blocks, with light rings that shrink all the way into the singularity, then collapses into an explosion crater that wipes out several chunks Run GLM 5.3 Flash locally in

GLM 5.3 Flash running locally makes a Minecraft black hole mod We ran GLM 5.3 Flash Q4 locally on a rented 4x RTX PRO 6000 box and asked it to make a lightning gun mod for the real game using the Fabric API Output: -7.6M tokens -Time: ~9 hours -Decode: ~96 tok/s The mod adds a black hole rifle, when shot it spawns a black hole that pulls in blocks, with light rings that shrink all the way into the singularity, then collapses into an explosion crater that wipes out several chunks Run GLM 5.3 Flash locally in

atomic.chat

31,042 просмотров • 2 дней назад

NVIDIA'S BIGGEST COMPETITOR AMD BUILT A BOX THAT RUNS A 235B MODEL AND KILLS $200 OPENAI AND $200 CLAUDE CODE FOR $9 A MONTH amd's ryzen ai max+ 395 is the first x86 chip that runs a 200 billion parameter model on a single piece of silicon with 128gb of unified memory the gmktec evo-x2 runs qwen3 235b fully, deepseek v3 comfortably and llama 3.3 70b with headroom. on linux you get 110gb of usable vram out of 128gb amd claimed the chip beat an nvidia rtx 5080 by more than 3x on deepseek r1 inference. a lunchbox sized pc outrunning a $1,000 discrete gpu on a real ai workload a heavy ai user pays $200 for claude code max, $200 for chatgpt pro, $20 for cursor and $20 for gemini. that's $5,280 a year and the box pays itself off in 9 to 10 months ollama installs in one command and claude code points at localhost so nothing leaves the machine and nothing costs per request bookmark this and read the article below

NVIDIA'S BIGGEST COMPETITOR AMD BUILT A BOX THAT RUNS A 235B MODEL AND KILLS $200 OPENAI AND $200 CLAUDE CODE FOR $9 A MONTH amd's ryzen ai max+ 395 is the first x86 chip that runs a 200 billion parameter model on a single piece of silicon with 128gb of unified memory the gmktec evo-x2 runs qwen3 235b fully, deepseek v3 comfortably and llama 3.3 70b with headroom. on linux you get 110gb of usable vram out of 128gb amd claimed the chip beat an nvidia rtx 5080 by more than 3x on deepseek r1 inference. a lunchbox sized pc outrunning a $1,000 discrete gpu on a real ai workload a heavy ai user pays $200 for claude code max, $200 for chatgpt pro, $20 for cursor and $20 for gemini. that's $5,280 a year and the box pays itself off in 9 to 10 months ollama installs in one command and claude code points at localhost so nothing leaves the machine and nothing costs per request bookmark this and read the article below

starmex

93,465 просмотров • 2 месяцев назад

This soldiering training is the most impressive and immersive I've ever tried. It is 10x better than a YouTube tutorial video. It really allowed me to see the procedure from all the points of view, and even get super close to get the details. It is a collaboration between @gracia_vr and Imperial College London: they recorded a soldering session with Gaussian Splatting Videos (4DGS), so that you can enjoy it from your VR headset. You can see the action happening in front of you; you can pause and re-watch what you need, change the point of view, get closer, get more distant. And the quality with which it has been shot is impressive: I enjoyed this experience with my DELL Pro Max Tower T2 with NVIDIA Pro RTX 6000, and I could really see all the small details of the PCB that was being soldered! I was really impressed by it. But I also noticed some drawbacks. First of all, some scenes have artifacts that make seeing the details of the PCB hard. I think when it comes to training involving small details, the capture and reproduction of the splat should be flawless. Then, as much as I loved it as a passive experience, I would have liked to have also some sort of practice session in VR. The power of VR is to let you learn by doing in full safety, and this kind of training does not exploit it. But maybe the best way to enjoy it would be in MR, where you have this training video close to a real workbench where you do the actual soldering while following the tutorial. Gaussian Splat can really revolutionize training: this kind of recording is much better than any flatscreen experience, and more accurate than any 3D CGI recostruction. I suggest you give it a go at this experience in the Gracia app (it's free). Then let me know your impressions! #VirtualReality #training #DellProPrecision #GaussianSplat #technology [Disclaimer: I'm a DELL Pro Precision Ambassador, and this is why I mentioned the model of my PC. I have been given a PC to do cool tests and share my results on social media. No monetary compensation or sales affiliation is part of the collaboration]

This soldiering training is the most impressive and immersive I've ever tried. It is 10x better than a YouTube tutorial video. It really allowed me to see the procedure from all the points of view, and even get super close to get the details. It is a collaboration between @gracia_vr and Imperial College London: they recorded a soldering session with Gaussian Splatting Videos (4DGS), so that you can enjoy it from your VR headset. You can see the action happening in front of you; you can pause and re-watch what you need, change the point of view, get closer, get more distant. And the quality with which it has been shot is impressive: I enjoyed this experience with my DELL Pro Max Tower T2 with NVIDIA Pro RTX 6000, and I could really see all the small details of the PCB that was being soldered! I was really impressed by it. But I also noticed some drawbacks. First of all, some scenes have artifacts that make seeing the details of the PCB hard. I think when it comes to training involving small details, the capture and reproduction of the splat should be flawless. Then, as much as I loved it as a passive experience, I would have liked to have also some sort of practice session in VR. The power of VR is to let you learn by doing in full safety, and this kind of training does not exploit it. But maybe the best way to enjoy it would be in MR, where you have this training video close to a real workbench where you do the actual soldering while following the tutorial. Gaussian Splat can really revolutionize training: this kind of recording is much better than any flatscreen experience, and more accurate than any 3D CGI recostruction. I suggest you give it a go at this experience in the Gracia app (it's free). Then let me know your impressions! #VirtualReality #training #DellProPrecision #GaussianSplat #technology [Disclaimer: I'm a DELL Pro Precision Ambassador, and this is why I mentioned the model of my PC. I have been given a PC to do cool tests and share my results on social media. No monetary compensation or sales affiliation is part of the collaboration]

TonyVT SkarredGhost

38,264 просмотров • 24 дней назад

NVIDIA CEO JENSEN HUANG UNVEILED THE RTX 3090 AND THE USED VERSION ON EBAY NOW KILLS A $400/MONTH CLAUDE CODE AND OPENAI BILL the rtx 3090 has 24gb of vram, the same as a new rtx 4090 that costs $2,000. for local ai inference vram matters more than anything else and the 3090 sells for $650-800 used it runs qwen 3.6 27b comfortably, deepseek r1 14b at 60+ tokens per second and mistral 7b for fast automation. 16gb of qwen 3.6 fits with 8gb of headroom for long context alibaba's qwen 3.6 27b beats claude 4.5 opus on realworldqa vision 84.1 vs 77.0 and on ifbench instructions 76.5 vs 58.0. a free open weight model out-benchmarks anthropic's flagship a heavy ai user pays $200 for claude code max, $200 for chatgpt pro, $20 for cursor and $20 for gemini. that's $5,280 a year and a used 3090 pays itself off in 3.5 months install ollama, pull qwen 3.6 27b, point claude code at localhost. same interface, nothing leaves the machine, nothing costs per request bookmark this and read the article below

NVIDIA CEO JENSEN HUANG UNVEILED THE RTX 3090 AND THE USED VERSION ON EBAY NOW KILLS A $400/MONTH CLAUDE CODE AND OPENAI BILL the rtx 3090 has 24gb of vram, the same as a new rtx 4090 that costs $2,000. for local ai inference vram matters more than anything else and the 3090 sells for $650-800 used it runs qwen 3.6 27b comfortably, deepseek r1 14b at 60+ tokens per second and mistral 7b for fast automation. 16gb of qwen 3.6 fits with 8gb of headroom for long context alibaba's qwen 3.6 27b beats claude 4.5 opus on realworldqa vision 84.1 vs 77.0 and on ifbench instructions 76.5 vs 58.0. a free open weight model out-benchmarks anthropic's flagship a heavy ai user pays $200 for claude code max, $200 for chatgpt pro, $20 for cursor and $20 for gemini. that's $5,280 a year and a used 3090 pays itself off in 3.5 months install ollama, pull qwen 3.6 27b, point claude code at localhost. same interface, nothing leaves the machine, nothing costs per request bookmark this and read the article below

starmex

86,410 просмотров • 2 месяцев назад

I really don't post wins much on this app 🤣🤣 it feels good being low-key. Although, let's doxx this one!. Most of the time, I win big, I reinvest, win again, reinvest. I barely spend on myself much, mostly spend my money helping people, or supporting the needy. It's time for me 😌. Hey NVIDIA GeForce Hey ROG Global I Just slapped a Brand new heavy machine in my bag! One of the most powerful laptops of 2025! Emphasis on BRAND NEW! Introducing, the new owner of the ASUS ROG STRIX G16 2025. SPECS: - Intel Core Ultra 9 275HX Processor - 32GB DDR5 RAM - Nvidia GeForce RTX™ 5080 Dedicated GPU with a massive 16GB DDR7 VRAM - 3TB PCIe 4.0 SSD NVME storage - 24 Cores CPU (16 E-cores + 8 P-cores) - Base Frequency 2.70GHz - Max Turbo Frequency 5.40GHz 6 Million Naira well spent on this bad boy. Now, let's lock-in again and run it up back turbo! I need my $U bag soon enough 🤭. Pitch me on to node running, and prover projects, I'm fully locked in.

🐐𝙲𝚛𝚢𝚙𝚝𝚘𝙳𝚛𝚊𝚐𝚘𝚗「🦑」

19,298 просмотров • 1 год назад

5. Dead Meat: A Murder Mystery Game Where You Control the Conversation Dead Meat by Meaning Machine is an upcoming murder mystery game where you can ask suspects anything—whether by voice or keyboard. Want to question their alibi, explore deep topics, or even confess your love? Your words shape the game. Persuade, manipulate, threaten, or charm your way to the truth. The interrogation room is your sandbox, and your imagination is the only limit. At CES 2025, Dead Meat was shown running in real-time on a GeForce RTX 50 Series GPU, generating dialogue locally for the first time. Previously, it used cloud-based LLMs, but thanks to a partnership with NVIDIA, the game now runs directly on your device.

5. Dead Meat: A Murder Mystery Game Where You Control the Conversation Dead Meat by Meaning Machine is an upcoming murder mystery game where you can ask suspects anything—whether by voice or keyboard. Want to question their alibi, explore deep topics, or even confess your love? Your words shape the game. Persuade, manipulate, threaten, or charm your way to the truth. The interrogation room is your sandbox, and your imagination is the only limit. At CES 2025, Dead Meat was shown running in real-time on a GeForce RTX 50 Series GPU, generating dialogue locally for the first time. Previously, it used cloud-based LLMs, but thanks to a partnership with NVIDIA, the game now runs directly on your device.

Mario Nawfal

24,670 просмотров • 1 год назад