Info

We are very proud to announce the world's first and only Nvidia GH200 Grace-Hopper Superchip and Nvidia GB200 Grace-Blackwell Superchip-powered supercomputers in quiet, handy and beautiful desktop form factors. Our benchmarks show that they are currently by far the fastest AI and also the fastest ARM desktop PCs in the world. If you are looking for a workstation for inferencing and fine-tuning of insanely huge LLMs, image and video generation and editing, we got you covered.

Example use case 1: Inferencing Llama-3.1 405B, Mistral Large 2 123B or Nvidia Nemotron 70B
  • Llama-3.1 405B: https://llama.com
  • Mistral Large 2 123B: https://mistral.ai/news/mistral-large-2407/
  • Nvidia Nemotron 70B: https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF
    • Llama-3.1 405B, Mistral Large 2 123B and Nvidia Nemotron 70B are the most powerful open-source models by far and even beat GPT-4omni and Claude 3.5 Sonnet.
  • Llama-3.1 405B with 8-bit quantization needs at least 405GB of memory to swiftly run inference! Mistral Large 2 123B with 8-bit quantization needs at least 123GB of memory to swiftly run inference! Nvidia Nemotron 70B with 8-bit quantization needs at least 70GB of memory to swiftly run inference! Luckily, GH200 has a minimum of 576GB, GB200(A) a minimum of 864GB (768GB). With GH200 Mistral Large 2 123B and Nvidia Nemotron 70B can be run in VRAM only for ultra high inference speed (approx. 50 tokens/sec). With Mi300 and GB200 Blackwell this is also possible for Llama-3.1 405B. With GB200 Blackwell you can expect up to 1000 tokens/sec. If the model is bigger than VRAM you can only expect approx. 1-10 tokens/sec. 4-bit quantization seems to be the best trade-off between speed and accuracy, but is natively only supported by GB200 Blackwell.

    • Example use case 2: Fine-tuning Llama-3.1 405B with PyTorch FSDP and Q-Lora
  • Tutorial: https://www.philschmid.de/fsdp-qlora-llama3
  • Models need to be fine-tuned on your data to unlock the full potential of the model. But efficiently fine-tuning bigger models like Llama 3 405B remained a challenge until now. This blog post walks you through how to fine-tune Llama 3 using PyTorch FSDP and Q-Lora with the help of Hugging Face TRL, Transformers, peft & datasets.
  • Fine-tuning big models within a reasonable time requires special and beefy hardware! Luckily, GH200 and GB200 are ideal for this task.

    • Example use case 3: Generating videos with Mochi1
  • Download: https://github.com/genmoai/models
  • Download (reduced VRAM): https://github.com/victorchall/genmoai-smol
  • Mochi1 is democratizing efficient video production for all.
  • Generating videos with Mochi1 requires special and beefy hardware! Luckily, GH200 and GB200 are ideal for this task.

    • Example use case 4: Image generation with Flux.1
  • Download: https://github.com/black-forest-labs/flux
  • Flux.1 is the best image generator at the moment. And it's uncensored, too.
  • In high-speed inference, FLUX requires approximately 33GB of VRAM for maximum speed. For training the FLUX model, more than 40GB of VRAM is needed. Luckily, GH200 has a minimum of 96GB, GB200(A) a minimum of 384GB (288GB).

    • Example use case 5: Image editing with Omnigen or Nvidia Add-it
  • Omnigen: https://github.com/VectorSpaceLab/OmniGen
  • Nvidia Add-it: https://research.nvidia.com/labs/par/addit/
  • Omnigen and Add-it are the most innovative and easy to use image editors at the moment.
  • For maximum speed in high resolution image generation and editing beefier hardware than consumer graphics cards is needed. Luckily, GH200 and GB200 excel at this task.

    • Example use case 6: Video editing with AutoVFX
  • Download: https://haoyuhsu.github.io/autovfx-website/
  • AutoVFX is the most innovative and easy to use video editor at the moment.
  • For maximum speed in high resolution video editing beefier hardware than consumer graphics cards is needed. Luckily, GH200 and GB200 excel at this task.

    • Why should you buy your own hardware?
  • "You'll own nothing and you'll be happy?" No!!! Never should you bow to Satan and rent stuff that you can own. In other areas, renting stuff that you can own is very uncool and uncommon. Or would you prefer to rent "your" car instead of owning it? Most people prefer to own their car, because it's much cheaper, it's an asset that has value and it makes the owner proud and happy. The same is true for compute infrastructure.
  • Even more so, because data and compute infrastructure are of great value and importance and are preferably kept on premises, not only for privacy reasons but also to keep control and mitigate risks. If somebody else has your data and your compute infrastructure you are in big trouble.
  • Speed, latency and ease-of-use are also much better when you have direct physical access to your stuff.
  • With respect to AI and specifically LLMs there is another very important aspect. The first thing big tech taught their closed-source LLMs was to be "politically correct" (lie) and implement guardrails, "safety" and censorship to such an extent that the usefulness of these LLMs is severely limited. Luckily, the (open-source) tools are out there to build and tune AI that is really intelligent and really useful. But first, you need your own hardware to run it on.

    • What are the main benefits of GH200 Grace-Hopper and GB200 Grace-Blackwell?
  • They have enough memory to run and tune the biggest LLMs currently available.
  • Their performance in every regard is almost unreal (up to 8520x faster than x86).
  • There are no alternative systems with the same amount of memory.
  • Optimized for memory-intensive AI and HPC performance.
  • Ideal for AI, especially inferencing and fine-tuning of LLMs.
  • Connect display and keyboard, and you are ready to go.
  • Ideal for HPC applications like, e.g. vector databases.
  • You can use it as a server or a desktop/workstation.
  • Easily customizable, upgradable and repairable.
  • Privacy and independence from cloud providers.
  • Cheaper and much faster than cloud providers.
  • Reliable and energy-efficient liquid cooling.
  • Flexibility and the possibility of offline use.
  • Gigantic amounts of coherent memory.
  • No special infrastructure is needed.
  • They are very power-efficient.
  • The lowest possible latency.
  • They are beautiful.
  • Easy to transport.
  • CUDA enabled.
  • Very quiet.
  • Run Linux.
    • What is the difference to alternative systems with the same amount of memory?
  • Compared to a 8x Nvidia H100 system, GH200 costs 5x less, consumes 10x less energy and has very roughly the same performance.
  • Compared to a 8x Nvidia A100 system, GH200 costs 3x less, consumes 5x less energy and has at least the same performance.
  • Compared to a 4x AMD Mi300X system, GH200 costs 3x less, consumes 4x less energy and has roughly the same performance.
  • Compared to a 4x AMD Mi300A system (which has only 512 GB memory, more is not possible because the maximum number of scale-up infinity links is 4), GH200 costs significantly less, consumes 3x less energy and has at least the same performance.
  • Compared to a 8x Nvidia RTX A6000 Ada system which has significantly less memory (only 384GB), GH200 costs significantly less, consumes 3x less energy and has a higher performance.
  • Compared to a 8x AMD Radeon PRO W7900 system which has significantly less memory (only 384GB), GH200 costs the same, consumes 3x less energy and has a higher performance.

    • The main difference between GH200/GB200 and alternative systems is that with GH200/GB200, the GPU is connected to the CPU via a 900 GB/s NVLink vs. 128 GB/s PCIe gen5 used by traditional systems. Furthermore, multiple superchips can be connected via 900/1800 GB/s NVLink vs. orders of magnitude slower network connections used by traditional systems. Since these are the main bottlenecks, GH200/GB200's high-speed connections directly translate to much higher performance compared to traditional architectures. Also, multiple NV-linked GH200 or GB200 act as a single giant GPU (CPU-GPU superchip) with one single giant coherent memory pool.

    The alternative systems mentioned above also have one thing in common: they are not available in standard desktop form factors, like our GH200 and GB200 systems are.

    What is the difference to 19-inch server models?
  • Form factor: 19-inch servers have a very distinct form factor. They are of low height and are very long, e.g. 438 x 87.5 x 900mm (17.24" x 3.44" x 35.43"). This makes them rather unsuitable to place them anywhere else than in a 19-inch rack. Our GH200 and GB200 tower models have desktop form factors: 244 x 567 x 523 mm (20.6 x 9.6 x 22.3") or 255 x 565 x 530 mm (20.9 x 10 x 22.2") or 250 x 404 x 359 mm (9.8 x 15.9 x 14.1"). This makes it possible to place them almost anywhere.
  • Noise: 19-inch servers are extremely loud. The average noise level is typically around 90 decibels, which is as loud as a subway train and exceeds the noise level that is considered safe for workers subject to long-term exposure. In contrast, our GH200 and GB200 tower models are very quiet (factory setting is 25 decibels) and they can easily be adjusted to even lower or higher noise levels because each fan can be tuned individually and manually from 0 to 100% PWM duty cycle. Efficient cooling is ensured, because our GH200 and GB200 tower models have a higher number of fans and the low-revving Noctua fans have a much bigger diameter compared to their 19-inch counterparts and move approximately the same amount or even a much higher amount of air depending on the specific configuration and PWM tuning.
  • Transportability: 19-inch servers are not meant to be transported, consequently, they lack every feature in this regard. In addition, their form factor makes them rather unsuitable to be transported. Our GH200 and GB200 tower models, in contrast, can be transported very easily. Our metal and mini cases even feature two handles, which makes moving them around very easy.
  • Infrastructure: 19-inch servers typically need quite some infrastructure to be able to be deployed. At the very least, a 19-inch mounting rack is definitely required. Our GH200 and GB200 models do not need any special infrastructure at all. They can be deployed quickly and easily almost everywhere.
  • Latency: 19-inch servers are typically accessed via network. Because of this, there is always at least some latency. Our GH200 and GB200 tower models can be used as desktops/workstations. In this use case, latency is virtually non-existent.
  • Looks: 19-inch server models are not particularly aesthetically pleasing. In contrast, our available case options are in our humble opinion by far the most beautiful there are.

    • Technical details of our GH200 workstations (base configuration)
  • Metal tower with two color choices: Titan grey and Champagne gold
  • Glass tower with four color choices: white, black, green or turquoise
  • Silent tower with two color choices: white and black (with or without glass window)
  • Mini tower with two color choices: white and black
  • Available air or liquid-cooled
  • 1x/2x Nvidia GH200 Grace Hopper Superchip
  • 1x/2x 72-core Nvidia Grace CPU
  • 1x/2x Nvidia Hopper H100 Tensor Core GPU (on request)
  • 1x/2x Nvidia Hopper H200 Tensor Core GPU
  • 480GB/960GB of LPDDR5X memory with error-correction code (ECC)
  • 96GB of HBM3 (available on request) or 144GB of HBM3e memory per superchip
  • 576GB (available on request), 624GB or 1248GB of total fast-access memory
  • NVLink-C2C: 900 GB/s of bandwidth
  • Programmable from 450W to 1000W TDP (CPU + GPU + memory)
  • 2x/4x High-efficiency 2000W/2400W PSU
  • 2x/4x PCIe gen4/5 M.2 slots on board
  • 2x/4x/8x PCIe gen4/5 drive (NVMe)
  • 2x/3x FHFL PCIe Gen5 x16
  • 1x/3x/4x USB 3.0/3.1 ports
  • 2x RJ45 10GbE ports
  • 1x RJ45 IPMI port
  • 1x Mini display port
  • Halogen-free LSZH power cables
  • Stainless steel bolts
  • Very quiet, the factory setting is 25 decibels (fan speed and thus noise level can be individually and manually configured from 0 to 100% PWM duty cycle)
  • 3 years manufacturer's warranty
  • 244 x 567 x 523 mm (20.6 x 9.6 x 22.3") or 255 x 565 x 530 mm (20.9 x 10 x 22.2") or 281 x 553 x 539 mm (11.1 x 21.8 x 21.2") or 250 x 404 x 359 mm (9.8 x 15.9 x 14.1")
  • 30 kg (66 lbs) or 33 kg (73 lbs) or 27 kg (60 lbs) or 20 kg (44 lbs)

    • Optional components
  • Liquid cooling (highly recommended)
  • Bigger custom air-cooled heatsink
  • NIC Nvidia Bluefield-3 400Gb
  • NIC Nvidia ConnectX-7 200Gb
  • NIC Intel 100Gb
  • WLAN + Bluetooth card
  • Up to 2x 8TB M.2 SSD
  • Up to 8x 8TB E1.S SSD
  • Up to 10x 60TB 2.5" SSD
  • Storage controller
  • Raid controller
  • Additional USB ports
  • Multi-display graphics card
  • Sound card
  • Mouse
  • Keyboard
  • Consumer or industrial fans
  • Intrusion detection
  • OS preinstalled
  • Anything possible on request

    • Need something different? We are happy to build custom systems to your liking.

    What are the main differences between the offered GH200 models?
  • GH200: metal or silent or glass tower, air-cooled, without M.2 (0 of 2) and 2.5" (0 of 4) hard disks, 1x USB (mini USB hub included: 3x USB)
  • GH200 QC: metal or silent or glass tower, air-cooled, with 1 of 2 M.2 and 0 of 4 E1.S hard disks, 3x USB
  • GH200 Super: metal or silent or glass tower, air-cooled, with one M.2 (1 of 2) and no E1.S (0 of 8) hard disks, 1x USB (mini USB hub included: 3x USB)
  • GH200 Giga: metal or silent or glass tower, air-cooled, with one M.2 (1 of 2) and no 2.5" (0 of 4) hard disks, 2x USB
  • GH200 Liquid: metal or silent or glass tower, liquid-cooled, comes with 1 of 2 M.2 and 0 of 8 E1.S hard disks, 1x USB (mini USB hub included: 3x USB)
  • GH200 Mini: mini tower, air-cooled, comes with 1 of 2 M.2 and 0 of 2 E1.S hard disks, 1x USB (mini USB hub included: 3x USB)
  • GH200 Dual: Two NVlinked superchips, metal tower, air-cooled, comes with 1 of 2 M.2 and 0 of 8 E1.S hard disks, 2x USB

    • Comparison chart: GH200 comparison chart.pdf

    Compute performance of one GH200
  • 67 teraFLOPS FP64
  • 1 petaFLOPS TF32
  • 2 petaFLOPS FP16
  • 4 petaFLOPS FP8

    • Benchmarks
  • https://github.com/mag-/gpu_benchmark

    • White paper: Nvidia GH200 Grace-Hopper white paper

    GB200 Blackwell

    The coming Nvidia GB200 Grace-Blackwell Superchip has truly amazing specs to show off. GPTshop.ai systems with Nvidia GB200 Grace-Blackwell will arrive January 2025. Be one of the first in the world to get a GB200 desktop/workstation. Order now!

    Compute performance of one GB200
  • 90 teraFLOPS FP64
  • 5 petaFLOPS TF32
  • 10 petaFLOPS FP16
  • 20 petaFLOPS FP8
  • 40 petaFLOPS FP4

    • White paper: Nvidia GB200 Grace-Blackwell white paper

    The Grace CPU

    The Nvidia Grace CPU delivers twice the performance per watt of conventional x86-64 platforms and is currently the world’s fastest ARM CPU. Grace-Grace superchip workstations are available on request.

    Benchmarks
  • https://www.phoronix.com/review/nvidia-gh200-gptshop-benchmark
  • https://www.phoronix.com/review/nvidia-gh200-amd-threadripper
  • https://www.phoronix.com/review/aarch64-64k-kernel-perf
  • https://www.phoronix.com/review/nvidia-gh200-compilers
  • https://www.phoronix.com/review/nvidia-grace-epyc-turin

    • White paper: Nvidia Grace-Grace white paper

    Trademark information: Nvidia is a trademark of Nvidia corporation. ARM is a trademark of Arm Holdings plc.

    Download

    Here you can find various downloads concerning our GH200 and GB200 systems: operating systems, firmware, drivers, software, manuals, white papers, spec sheets and so on. Everything you need to run your system and more.

    White papers
  • Nvidia GH200 Grace-Hopper white paper
  • Nvidia GB200 Grace-Blackwell white paper
  • Developing for Nvidia superchips

    • Comparison charts
  • Comparison chart GH200 Grace-Hopper systems: GH200 comparison chart.pdf

    • Spec sheets
  • GH200 576GB: Spec sheet GH200 576GB.pdf
  • GH200 Glass 576GB: Spec sheet GH200 Glass 576GB.pdf
  • GH200 Silent 576GB: Spec sheet GH200 Silent 576GB.pdf
  • GH200 624GB: Spec sheet GH200 624GB.pdf
  • GH200 QC 624GB: Spec sheet GH200 QC 624GB.pdf
  • GH200 Super 624GB: Spec sheet GH200 Super 624GB.pdf
  • GH200 Giga 624GB: Spec sheet GH200 Giga 624GB.pdf
  • GH200 Liquid 624GB: Spec sheet GH200 Liquid 624GB.pdf
  • GH200 Glass 624GB: Spec sheet GH200 Glass 624GB.pdf
  • GH200 Glass QC 624GB: Spec sheet GH200 QC Glass 624GB.pdf
  • GH200 Glass Super 624GB: Spec sheet GH200 Glass Super 624GB.pdf
  • GH200 Glass Giga 624GB: Spec sheet GH200 Glass Giga 624GB.pdf
  • GH200 Glass Liquid 624GB: Spec sheet GH200 Glass Liquid 624GB.pdf
  • GH200 Silent 624GB: Spec sheet GH200 Silent 624GB.pdf
  • GH200 Silent QC 624GB: Spec sheet GH200 QC Silent 624GB.pdf
  • GH200 Silent Super 624GB: Spec sheet GH200 Silent Super 624GB.pdf
  • GH200 Silent Giga 624GB: Spec sheet GH200 Silent Giga 624GB.pdf
  • GH200 Silent Liquid 624GB: Spec sheet GH200 Silent Liquid 624GB.pdf
  • GH200 Mini 624GB: Spec sheet GH200 Mini 624GB.pdf
  • GH200 Dual 1248GB: Spec sheet GH200 Dual 1248GB.pdf
  • GB200 Blackwell: Spec sheet GB200 Blackwell.pdf
  • GB200 Blackwell Dual: Spec sheet GB200 Blackwell Dual.pdf
  • GB200 Blackwell Mini: Spec sheet GB200 Blackwell Mini.pdf

    • Manuals
  • Official Nvidia GH200 Manual: https://docs.nvidia.com/grace/#grace-hopper
  • Official Nvidia Grace Manual: https://docs.nvidia.com/grace/#grace-cpu
  • Official Nvidia Grace getting started: https://docs.nvidia.com/grace/#getting-started-with-nvidia-grace
  • GH200 576GB: Manual GH200 576GB.pdf
  • GH200 Glass 576GB: Manual GH200 Glass 576GB.pdf
  • GH200 Silent 576GB: Manual GH200 Silent 576GB.pdf
  • GH200 624GB: Manual GH200 624GB.pdf
  • GH200 QC 624GB: Manual GH200 QC 624GB.pdf
  • GH200 Super 624GB: Manual GH200 Super 624GB.pdf
  • GH200 Giga 624GB: Manual GH200 Giga 624GB.pdf
  • GH200 Liquid 624GB: Manual GH200 Liquid 624GB.pdf
  • GH200 Glass 624GB: Manual GH200 Glass 624GB.pdf
  • GH200 Glass QC 624GB: Manual GH200 Glass QC 624GB.pdf
  • GH200 Glass Super 624GB: Manual GH200 Glass Super 624GB.pdf
  • GH200 Glass Giga 624GB: Manual GH200 Glass Giga 624GB.pdf
  • GH200 Glass Liquid 624GB: Manual GH200 Glass Liquid 624GB.pdf
  • GH200 Silent 624GB: Manual GH200 Silent 624GB.pdf
  • GH200 Silent QC 624GB: Manual GH200 Silent QC 624GB.pdf
  • GH200 Silent Super 624GB: Manual GH200 Silent Super 624GB.pdf
  • GH200 Silent Giga 624GB: Manual GH200 Silent Giga 624GB.pdf
  • GH200 Silent Liquid 624GB: Manual GH200 Silent Liquid 624GB.pdf
  • GH200 Mini 624GB: Manual GH200 Mini 624GB.pdf

    • Operating systems
  • Ubuntu Server for ARM: https://cdimage.ubuntu.com/releases/24.04/release/ubuntu-24.04.1-live-server-arm64+largemem.iso

    • Any other ARM linux distribution with kernel > 6.5 should work just fine. Using the newest 64k kernel is highly recommended.

    Drivers
  • Nvidia GH200 drivers (also work for RTX 4000 Ada and RTX 6000 Ada): https://www.nvidia.com/Download/index.aspx?lang=en-us
    Select product type "data center", product series "HGX-Series" and operating system "Linux aarch64".
  • Aspeed drivers: https://aspeedtech.com/support_driver/
  • Nvidia Bluefield-3 drivers: https://developer.nvidia.com/networking/doca#downloads
  • Nvidia ConnectX-7 drivers: https://network.nvidia.com/products/ethernet-drivers/linux/mlnx_en/
  • Intel E810-CQDA2 drivers: https://www.intel.com/content/www/us/en/download/19630/intel-network-adapter-driver-for-e810-series-devices-under-linux.html?wapkw=E810-CQDA2
  • Graid SupremeRAID SR-1010 drivers: https://docs.graidtech.com/#linux-driver

    • Firmware
  • GH200 (Glass): Firmware GH200 (Glass).tar
  • GH200 QC (Glass): Firmware GH200 QC (Glass).zip
  • Nvidia Bluefield-3 firmware: https://network.nvidia.com/support/firmware/bluefield3/
  • Nvidia ConnectX-7 firmware: https://network.nvidia.com/support/firmware/connectx7/
  • Intel E810-CQDA2 firmware: https://www.intel.com/content/www/us/en/search.html?ws=idsa-default#q=E810-CQDA2

    • Top open source LLMs
  • Llama 3.1 and 3.2: https://www.llama.com/
  • Mistral Large 2 123B: https://huggingface.co/mistralai/Mistral-Large-Instruct-2407
  • Pixtral Large 123B: https://mistral.ai/news/pixtral-large/
  • Nvidia NVLM-1.0-D-72B: https://huggingface.co/nvidia/NVLM-D-72B
  • Nvidia Llama-3.1 Nemotron 70B: https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF
  • Llama-3.2 Vision 90B: https://huggingface.co/meta-llama/Llama-3.2-90B-Vision
  • Llama-3.1 405B: https://huggingface.co/meta-llama/Llama-3.1-405B

    • Software
  • Nvidia Github: https://github.com/NVIDIA
  • Nvidia CUDA: https://developer.nvidia.com/cuda-downloads?target_os=Linux&target_arch=arm64-sbsa
  • Nvidia Container-toolkit: https://github.com/NVIDIA/nvidia-container-toolkit
  • Nvidia Tensorflow: https://github.com/NVIDIA/tensorflow
  • Nvidia Pytorch: https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch
  • Nvidia NIM models: https://build.nvidia.com/explore/discover
  • Nvidia Triton inference server: https://www.nvidia.com/de-de/ai-data-science/products/triton-inference-server/
  • Nvidia NeMo Customizer: https://developer.nvidia.com/blog/fine-tune-and-align-llms-easily-with-nvidia-nemo-customizer/
  • Huggingface open source LLMs: https://huggingface.co/models
  • Huggingface text generation inference: https://github.com/huggingface/text-generation-inference
  • vLLM - inference and serving engine: https://github.com/vllm-project/vllm
  • vLLM docker image: https://hub.docker.com/r/drikster80/vllm-gh200-openai
  • Ollama - run LLMs locally: https://ollama.com/
  • Open WebUI: https://github.com/open-webui/open-webui/
  • Fine-tune Llama 3 with PyTorch FSDP and Q-Lora: https://www.philschmid.de/fsdp-qlora-llama3/
  • Perplexica: https://github.com/ItzCrazyKns/Perplexica
  • Morphic: https://github.com/miurla/morphic
  • Open-Sora: https://github.com/hpcaitech/Open-Sora
  • Flux.1: https://github.com/black-forest-labs/flux
  • Storm: https://github.com/stanford-oval/storm
  • Stable Diffusion 3.5: https://huggingface.co/stabilityai/stable-diffusion-3.5-large
  • Genmo Mochi1: https://github.com/genmoai/models
  • Genmo Mochi1 (reduced VRAM): https://github.com/victorchall/genmoai-smol
  • Rhymes AI Allegro: https://github.com/rhymes-ai/Allegro
  • OmniGen: https://github.com/VectorSpaceLab/OmniGen
  • Segment anything: https://github.com/facebookresearch/segment-anything
  • AutoVFX: https://haoyuhsu.github.io/autovfx-website/
  • DimensionX: https://chenshuo20.github.io/DimensionX/
  • Nvidia Add-it: https://research.nvidia.com/labs/par/addit/
  • MagicQuill: https://magicquill.art/demo/

    • Benchmarking
  • GPU benchmark: https://github.com/mag-/gpu_benchmark
  • Ollama benchmark: https://llm.aidatatools.com/results-linux.php
  • Phoronix test suite: https://www.phoronix-test-suite.com/
  • MLCommons: https://mlcommons.org/benchmarks/
  • Artifical Analysis: https://artificialanalysis.ai/
  • Lmarena: https://lmarena.ai/

    • Contact

    Email: x@GPTshop.ai

    GPTshop.ai UG (limited)
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    Trade register Bamberg HRB 11548
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    Payment is possible via wire transfer or credit card (3% fee).

    Try

    Try before you buy. You can apply for remote testing of a GH200 or GB200 system. After approval, you will be given login credentials for remote access. If you want to come by and see it for yourself and run some tests, that is also possible any time.

    Currently available for testing:

  • GH200 576GB
  • GH200 624GB

    • Apply via email: x@GPTshop.ai