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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 просмотров • 4 месяцев назад •via X (Twitter)

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

Anya Rossi• Live Now

Private livecam show

Комментарии: 27

Фото профиля Ufuoma Desmond

Ufuoma Desmond4 месяцев назад

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?

Фото профиля CZ Calls

CZ Calls4 месяцев назад

Development speed is impressive let’s reach out 🔥

Фото профиля ĐếVương | Lowkey

ĐếVương | Lowkey2 месяцев назад

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

Фото профиля Jay

Jay3 месяцев назад

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

Фото профиля Jay

Jay3 месяцев назад

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

Фото профиля الأمــيـرَةُ_𝑨𝒍_𝑨𝒎𝒊𝒓𝒂𝒉

الأمــيـرَةُ_𝑨𝒍_𝑨𝒎𝒊𝒓𝒂𝒉3 месяцев назад

Send me a private message

Фото профиля Bwire foundation

Bwire foundation3 месяцев назад

We are kindly requesting for your support brother and sister 🙏

Фото профиля BobVoyage (💙,🧡) | ⛽

BobVoyage (💙,🧡) | ⛽3 месяцев назад

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

Фото профиля Victoria Blake

Victoria Blake2 месяцев назад

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! 🤝

Фото профиля Ummi

Ummi2 месяцев назад

Nice

Фото профиля 0xdefilink

0xdefilink3 месяцев назад

i cant start a 💬 w u, can u try?

Фото профиля Aina Ai | Tools & Updates

Aina Ai | Tools & Updates4 месяцев назад

Very nice

Фото профиля Ngọc My

Ngọc My1 месяц назад

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

Фото профиля Sysracks

Sysracks1 месяц назад

Soo cool 👍

Фото профиля مالكت 💃🏻تويتر 😱

مالكت 💃🏻تويتر 😱2 месяцев назад

تم

Фото профиля Jason Crypto

Jason Crypto2 месяцев назад

This has huge potential. My DMs are open 🤝

Фото профиля NurturerGlow

NurturerGlow3 месяцев назад

Me as @niyirora654 i like your contents alot

Фото профиля dody

dody2 месяцев назад

هلا

Фото профиля Khon Bhai

Khon Bhai1 месяц назад

DM me for social media marketing at instagram

Фото профиля Priest

Priest3 месяцев назад

Nice

Фото профиля Mr. 𝗡𝗔𝗹𝗼𝗻𝗲 🥀

Mr. 𝗡𝗔𝗹𝗼𝗻𝗲 🥀3 месяцев назад

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

Фото профиля Kachis

Kachis4 месяцев назад

This is amazing

Фото профиля Bit Nexus

Bit Nexus2 месяцев назад

End today with grateful thoughts

Фото профиля Khing Sãffûł

Khing Sãffûł1 месяц назад

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

Фото профиля Thais Coelho

Thais Coelho3 месяцев назад

can you start a 💬?

Фото профиля Lương Nguyễn

Lương Nguyễn2 месяцев назад

G

Фото профиля Lee_1909

Lee_19092 месяцев назад

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

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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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InterServer

26,649 просмотров • 10 месяцев 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 • 7 месяцев назад

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

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 просмотров • 27 дней назад

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 просмотров • 3 месяцев назад

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 просмотров • 22 дней назад

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

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

stevibe

197,300 просмотров • 6 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

193,239 просмотров • 5 месяцев назад

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 просмотров • 6 месяцев 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 • 1 месяц назад

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 просмотров • 7 месяцев назад

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 просмотров • 4 месяцев назад

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 просмотров • 2 месяцев назад

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 просмотров • 26 дней назад