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Building RTX 6000 PRO GPU Server #GPUServer #RTX6000 #TechInnovation #DataCenter #HighPerformance #ServerBuild #AIHardware #CloudComputing #InterServer #TechSolutions

InterServer

59,636 subscribers

98,662 次观看 • 4 个月前 •via X (Twitter)

#GPUServer #RTX6000 #TechInnovation #DataCenter #HighPerformance #ServerBuild #AIHardware #CloudComputing #InterServer #TechSolutions

Anya Rossi• Live Now

Private livecam show

27 条评论

Ufuoma Desmond 的头像

Ufuoma Desmond4 个月前

You guys are a disgrace. I can't upload anything to my hosting and I though it was the WordPress installation I contact your Customer Support and I was told that there is no problem and I need developers figure it out. What kind of hosting company hires incompetent staff?

CZ Calls 的头像

CZ Calls4 个月前

Development speed is impressive let’s reach out 🔥

ĐếVương | Lowkey 的头像

ĐếVương | Lowkey2 个月前

mấy con rtx 6000 pro này nhìn ngon thiệt, hồi xưa mơ build mà giờ chỉ dám chơi con card đồ họa có quạt nhỏ hơn quạt bàn 😅 build ngon nha bác

Jay 的头像

Jay3 个月前

I’m not able to change the root password of my VPS…. Being waiting for an hour Oh GOD🤦🏻‍♂️

Jay 的头像

Jay3 个月前

@interserver help me change my root password Been waiting for more than an hour 🤦🏻‍♂️

الأمــيـرَةُ_𝑨𝒍_𝑨𝒎𝒊𝒓𝒂𝒉 的头像

الأمــيـرَةُ_𝑨𝒍_𝑨𝒎𝒊𝒓𝒂𝒉3 个月前

Send me a private message

Bwire foundation 的头像

Bwire foundation3 个月前

We are kindly requesting for your support brother and sister 🙏

BobVoyage (💙,🧡) | ⛽ 的头像

BobVoyage (💙,🧡) | ⛽3 个月前

Impressive setup! Do you share benchmarks from your builds regularly? 🔧💡

Victoria Blake 的头像

Victoria Blake2 个月前

Building high-performance AI infrastructure is key to powering the next generation of innovation. 🚀 We'd love to collaborate and help showcase InterServer's GPU solutions to a broader AI and developer audience. Looking forward to connecting! 🤝

Ummi 的头像

Ummi2 个月前

Nice

0xdefilink 的头像

0xdefilink3 个月前

i cant start a 💬 w u, can u try?

Aina Ai | Tools & Updates 的头像

Aina Ai | Tools & Updates4 个月前

Very nice

Ngọc My 的头像

Ngọc My1 个月前

The RTX 6000 PRO is a solid choice for AI workloads, should be a powerhouse for your server

Sysracks 的头像

Sysracks1 个月前

Soo cool 👍

مالكت 💃🏻تويتر 😱 的头像

مالكت 💃🏻تويتر 😱2 个月前

تم

Jason Crypto 的头像

Jason Crypto2 个月前

This has huge potential. My DMs are open 🤝

NurturerGlow 的头像

NurturerGlow3 个月前

Me as @niyirora654 i like your contents alot

dody 的头像

dody2 个月前

هلا

Khon Bhai 的头像

Khon Bhai1 个月前

DM me for social media marketing at instagram

Priest 的头像

Priest3 个月前

Nice

Mr. 𝗡𝗔𝗹𝗼𝗻𝗲 🥀 的头像

Mr. 𝗡𝗔𝗹𝗼𝗻𝗲 🥀3 个月前

"Powering the future of AI starts with the right infrastructure. 🔥 Wishing you a smooth build and top-tier performance! 🚀"

Kachis 的头像

Kachis4 个月前

This is amazing

Bit Nexus 的头像

Bit Nexus2 个月前

End today with grateful thoughts

Khing Sãffûł 的头像

Khing Sãffûł1 个月前

I've tried every means to very this thing but it's not working. How do you expect customers using local bank cards to get this done ? This is so poor

Thais Coelho 的头像

Thais Coelho3 个月前

can you start a 💬?

Lương Nguyễn 的头像

Lương Nguyễn2 个月前

G

Lee_1909 的头像

Lee_19092 个月前

Wow, an RTX 6000 PRO server? That's serious horsepower for AI.

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18,727 次观看 • 3 个月前

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32,160 次观看 • 1 个月前

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Four Intel Arc Pro B70s in one box: 32GB of GDDR6 each, 128GB total, about $4,000 in GPUs. In our testing the quad setup more than doubles the DGX Spark and only trails a single RTX Pro 6000 that is much more expensive. Full review on the site. #IntelArc #GPUs #AIHardware Intel

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67,572 次观看 • 2 个月前

Quick video on how we wire up new racks. Yellow cables are for the public network. Orange Cables are for the private network. Every cable is labeled with the corresponding port number.

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InterServer

26,649 次观看 • 10 个月前

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 次观看 • 7 个月前

$RTX 3090 was never designed to be a server GPU. Yet someone just built a 4-GPU AI workstation around four of them. 4× RTX 3090 96GB VRAM 256GB ECC RAM AMD EPYC 7K62 2000W PSU 11 fans The interesting part isn't the performance. It's the memory. Four 24GB cards can be found for a fraction of what you'd pay for modern datacenter GPUs with similar total VRAM. The catch is obvious: 96GB doesn't behave like one 96GB GPU. You still have to deal with GPU-to-GPU communication, power and heat. But for local AI, this is exactly why the 3090 refuses to disappear. Would you build this today?$

RTX 3090 was never designed to be a server GPU. Yet someone just built a 4-GPU AI workstation around four of them. 4× RTX 3090 96GB VRAM 256GB ECC RAM AMD EPYC 7K62 2000W PSU 11 fans The interesting part isn't the performance. It's the memory. Four 24GB cards can be found for a fraction of what you'd pay for modern datacenter GPUs with similar total VRAM. The catch is obvious: 96GB doesn't behave like one 96GB GPU. You still have to deal with GPU-to-GPU communication, power and heat. But for local AI, this is exactly why the 3090 refuses to disappear. Would you build this today?

KyzoroX

36,552 次观看 • 27 天前

Scale your AI inference, graphics, and analytics workloads with the GPU power to get you there. Amazon EC2 G7 instances are now generally available—accelerated by @NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs, first on AWS.

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Amazon Web Services

1,960,506 次观看 • 3 个月前

768GB of VRAM in one 4U server. That number sounds ridiculous until you remember it’s not one 768GB GPU. This Gigabyte G494-ZB4 packs: 8× RTX PRO 6000 Blackwell 768GB VRAM 2× EPYC 9554 1TB DDR5 ECC And that’s the part people often miss with multi-GPU systems. You can have almost 1TB of GPU memory, but the model still has to be split across 8 separate memory pools. So I think the next AI hardware race isn't just about how much memory you can put in a server. It's about how close that memory can behave to one giant pool. That’s a much harder problem to solve.

Sensitive content

768GB of VRAM in one 4U server. That number sounds ridiculous until you remember it’s not one 768GB GPU. This Gigabyte G494-ZB4 packs: 8× RTX PRO 6000 Blackwell 768GB VRAM 2× EPYC 9554 1TB DDR5 ECC And that’s the part people often miss with multi-GPU systems. You can have almost 1TB of GPU memory, but the model still has to be split across 8 separate memory pools. So I think the next AI hardware race isn't just about how much memory you can put in a server. It's about how close that memory can behave to one giant pool. That’s a much harder problem to solve.

KyzoroX

88,405 次观看 • 22 天前

Finally got my hands on the big one. Qwen3.5-122B-A10B — 122 billion parameters. Too big for any single consumer GPU. So I rented 4 of each... and then one professional card to see if brute force even matters. - 1x RTX PRO 6000 (96GB): 101.4 tok/s - 4x 5090 (128GB): 87.0 tok/s - 4x 4090 (96GB): 25.1 tok/s - 4x 3090 (96GB): 20.8 tok/s One single $8,500 card beat four RTX 5090s

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stevibe

197,300 次观看 • 6 个月前

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

193,239 次观看 • 5 个月前

Building my friend his dream pc 👀🫣 using the MSI Gaming USA RTX 5090 LIGHTNING Z 😍😍⚡️⚡️ what do you think?? 👀👀 those decals be popping! PC Parts List: CPU: Intel Core Ultra 7 265KF Motherboard: MSI MEG Z890 GODLIKE GPU: MSI RTX 5090 Lightning Z RAM: Corsair Dominator Titanium DDR5 SSD: MSI Spatium M480 Pro 2TB Cooler: MSI CoreLiquid P1 360 PSU: MSI AI1600T PCIE5 (1600W) Case: MSI MEG Maestro 700L PZ

Building my friend his dream pc 👀🫣 using the MSI Gaming USA RTX 5090 LIGHTNING Z 😍😍⚡️⚡️ what do you think?? 👀👀 those decals be popping! PC Parts List: CPU: Intel Core Ultra 7 265KF Motherboard: MSI MEG Z890 GODLIKE GPU: MSI RTX 5090 Lightning Z RAM: Corsair Dominator Titanium DDR5 SSD: MSI Spatium M480 Pro 2TB Cooler: MSI CoreLiquid P1 360 PSU: MSI AI1600T PCIE5 (1600W) Case: MSI MEG Maestro 700L PZ

Nay.Tesseract

22,354 次观看 • 6 个月前

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 次观看 • 1 个月前

Jak ze stacji roboczej HP - Z6 zrobić środowisko do vibe codingu? Przepis jest prosty - stacja robocza z 1x GPU (akurat moja posiada 2x Nvidia RTX PRO 6000 Blackewell Max-Q), lokalna instalacja vllm'a albo SGLang, najnowszy model Qwen3-Coder-30B-A3B-Instruct i żegnajcie limity, ograniczenia, płatności. 100% lokalnie, bez przekazywania danych, bezpiecznie. Zobaczmy, czy dzięki takiej stacji kilka osób jest w stanie pracować lokalnie. Oczywiście opencode. #HP #ZbyHP #HPIncPolska #StacjeRoboczeHP [płatna współpraca]

Jak ze stacji roboczej HP - Z6 zrobić środowisko do vibe codingu? Przepis jest prosty - stacja robocza z 1x GPU (akurat moja posiada 2x Nvidia RTX PRO 6000 Blackewell Max-Q), lokalna instalacja vllm'a albo SGLang, najnowszy model Qwen3-Coder-30B-A3B-Instruct i żegnajcie limity, ograniczenia, płatności. 100% lokalnie, bez przekazywania danych, bezpiecznie. Zobaczmy, czy dzięki takiej stacji kilka osób jest w stanie pracować lokalnie. Oczywiście opencode. #HP #ZbyHP #HPIncPolska #StacjeRoboczeHP [płatna współpraca]

Remek Kinas

21,533 次观看 • 7 个月前

watch gemma 4 12b q8 dancing on a single rtx 3090 at 33 tokens a second average. google dropped this two days ago and it's the kind of thing that quietly moves the floor. a fully multimodal model, text image and audio in one net, 256k context, apache licensed, running entirely on one consumer gpu, no one metering your tokens. what you're watching is the whole loop live: the server streaming tokens top left, the gpu pegged bottom left, the answer landing on the right. all local, all mine. a year ago this needed someone else's datacenter. today it's a card you can buy. open source isn't catching up anymore, it's setting the pace. how fast does yours run?

watch gemma 4 12b q8 dancing on a single rtx 3090 at 33 tokens a second average. google dropped this two days ago and it's the kind of thing that quietly moves the floor. a fully multimodal model, text image and audio in one net, 256k context, apache licensed, running entirely on one consumer gpu, no one metering your tokens. what you're watching is the whole loop live: the server streaming tokens top left, the gpu pegged bottom left, the answer landing on the right. all local, all mine. a year ago this needed someone else's datacenter. today it's a card you can buy. open source isn't catching up anymore, it's setting the pace. how fast does yours run?

Sudo su

36,614 次观看 • 4 个月前

384GB of VRAM. On your desk. Not in a datacenter. Autonomous, the company behind SmartDesk, just open sourced complete build guides for a personal AI computer that runs open models on hardware no one can switch off. It's called Autonomous Computer. Three configurations, one philosophy: a model you rent can be cut off overnight. A model running in your own house can't. → Home build: 2x RTX 5090 workstation → Team build: 4x RTX PRO 6000 Blackwell, 384GB VRAM → On-prem business build: 8x RTX 4090/5090, up to 256GB VRAM Every config ships with the full bill of materials, 3D printable and CNC housing files, wiring diagrams, BIOS tuning, and assembly photos. Software side covers OS setup, NVIDIA drivers, and serving open models through Ollama, vLLM, and llama.cpp. Fork it. Change it. Build it. Sell it. MIT License. 100% Opensource. Repo:

384GB of VRAM. On your desk. Not in a datacenter. Autonomous, the company behind SmartDesk, just open sourced complete build guides for a personal AI computer that runs open models on hardware no one can switch off. It's called Autonomous Computer. Three configurations, one philosophy: a model you rent can be cut off overnight. A model running in your own house can't. → Home build: 2x RTX 5090 workstation → Team build: 4x RTX PRO 6000 Blackwell, 384GB VRAM → On-prem business build: 8x RTX 4090/5090, up to 256GB VRAM Every config ships with the full bill of materials, 3D printable and CNC housing files, wiring diagrams, BIOS tuning, and assembly photos. Software side covers OS setup, NVIDIA drivers, and serving open models through Ollama, vLLM, and llama.cpp. Fork it. Change it. Build it. Sell it. MIT License. 100% Opensource. Repo:

Harman

48,089 次观看 • 2 个月前

Timelapse #181 (11.1 hrs) - assembled a fair bit of the 4x RTX Pro 6000 Blackwell server build live on stream - bottlenecked debugging motherboard post codes and waiting on multiple multimeter deliveries - ended up doing a dozen component swaps to isolate the faults - drove for the first time in 4 years to hit the gym and grab 99% isopropyl to clean contacts, then resumed stream - crashed out with the entire room a total mess - exhausting day of hardware troubleshooting, but deeply fulfilling

Timelapse #181 (11.1 hrs) - assembled a fair bit of the 4x RTX Pro 6000 Blackwell server build live on stream - bottlenecked debugging motherboard post codes and waiting on multiple multimeter deliveries - ended up doing a dozen component swaps to isolate the faults - drove for the first time in 4 years to hit the gym and grab 99% isopropyl to clean contacts, then resumed stream - crashed out with the entire room a total mess - exhausting day of hardware troubleshooting, but deeply fulfilling

Elliot Arledge

10,914 次观看 • 26 天前