GPTshop.ai

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Nothing is more valuable than intelligence. Luckily, inferencing, tuning and even training gigantic cutting-edge LLMs have become a commodity. Thanks to state-of-the-art, open-weight LLMs you can download for free, the only thing you need is suitable hardware. We are very proud to announce the world's first and only Nvidia GH200 Grace-Hopper Superchip-, B200 Blackwell, B300 Blackwell Ultra, Mi355X, GB200 Grace-Blackwell Superchip, GB300 Grace-Blackwell Ultra Superchip-powered supercomputers in quiet, handy and beautiful desktop form factors. They are currently by far the fastest AI desktop PCs in the world. We are very proud to be the only ones in the world to offer such extremly powerful systems. Our components are sourced only from the best and well-known manufacturers like Pegatron, ASUS, MSI, Asrock Rack, Gigabyte and Supermicro. If you are looking for a workstation for inferencing, fine-tuning and training of insanely huge LLMs, image and video generation and editing, as well as high-performance computing, we got you covered. We ship worldwide!

Example use case 1: Inferencing MiniMax M2.1 229B, Xiaomi MiMo V2 Flash 310B, ZAI GLM-4.7 358B, DeepSeek V3.2 Speciale 685B, Moonshot AI Kimi K2 1T Thinking
  • MiniMax M2.1 229B: https://huggingface.co/MiniMaxAI/MiniMax-M2.1
  • Xiaomi MiMo V2 Flash 310B: https://huggingface.co/XiaomiMiMo/MiMo-V2-Flash
  • ZAI GLM-4.7 358B: https://huggingface.co/zai-org/GLM-4.7
  • DeepSeek V3.2 Speciale 685B: https://huggingface.co/deepseek-ai/DeepSeek-V3.2-Speciale
  • Moonshot AI Kimi K2 1T Thinking: https://huggingface.co/moonshotai/Kimi-K2-Thinking
    • MiniMax M2.1 229B, Xiaomi MiMo V2 Flash 310B, ZAI GLM-4.7 358B, DeepSeek V3.2 Speciale 685B, Moonshot AI Kimi K2 1T Thinking are currently the most powerful open-source models by far and even match GPT 5.2, Claude 4.5 Opus and Gemini 3 Pro.
  • MiniMax M2.1 229B with 4-bit quantization needs at least 130GB of memory to swiftly run inference! Luckily, GH200 has a minimum of 624GB, GB300 a minimum of 775GB. With GH200, MiniMax M2.1 229B in 4-bit can be run in VRAM only for ultra-high inference speed (significantly more than 100 tokens/s). With (G)B200 Blackwell, as well as (G)B300 Blackwell Ultra this is also possible for Moonshot AI Kimi K2 1T Thinking. With (G)B200 Blackwell and (G)B300 Blackwell Ultra you can expect signifcantly more than 100 tokens/s. If the model is bigger than VRAM you can only expect approx. 20-50 tokens/s. 4-bit quantization is the best trade-off between speed and accuracy, but is natively only supported by Blackwell. We recommend using vLLM and Nvidia Dynamo for inferencing.
    • Example use case 2: Fine-tuning Moonshot AI Kimi K2 1T Thinking with PyTorch FSDP and Q-Lora
  • Tutorial: https://www.philschmid.de/fsdp-qlora-llama3
  • The ultimate guide to fine-tuning: https://arxiv.org/abs/2408.13296
  • Models need to be fine-tuned on your data to unlock the full potential of the model. But efficiently fine-tuning bigger models like Deepseek R1 0528 685B remained a challenge until now. This blog post walks you through how to fine-tune Deepseek R1 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, B200 and GB300 are ideal for this task.
    • Example use case 3: Generating videos with LTX-2, Mochi1, HunyuanVideo or Wan 2.2
  • Ligthtricks LTX-2: https://huggingface.co/Lightricks/LTX-2
  • Mochi1: https://github.com/genmoai/models
  • Tencent HunyuanVideo: https://aivideo.hunyuan.tencent.com/
  • Wan 2.2: https://wan.video/
  • LTX-2, Mochi1, HunyuanVideo and Wan 2.2 are democratizing efficient video production for all.
  • Generating videos requires special and beefy hardware! Mochi1 and HunyuanVideo need 80GB of VRAM. Luckily, GH200, B200 and GB300 are ideal for this task. GH200 has a minimum of 144GB, GB300 a minimum of 288GB and B200 has a minimum of 1.5TB.
    • Example use case 4: Image generation with Qwen-Image-2512, Flux 2 dev, Hunyuan Image 3.0, Z Image turbo, HiDream-I1, SANA-Sprint or SRPO.
  • Qwen-Image-2512: https://huggingface.co/Qwen/Qwen-Image-2512
  • Flux 2 dev: https://huggingface.co/black-forest-labs/FLUX.2-dev
  • Hunyuan Image 3.0: https://github.com/Tencent-Hunyuan/HunyuanImage-3.0
  • Z Image turbo: https://modelscope.cn/models/Tongyi-MAI/Z-Image-Turbo
  • HiDream-I1: https://github.com/HiDream-ai/HiDream-I1
  • SANA-Sprint: https://nvlabs.github.io/Sana/Sprint/
  • Tencent SRPO: https://github.com/Tencent-Hunyuan/SRPO
  • Flux 2 dev, Hunyuan Image 3.0, Z Image turbo, HiDream-I1, SANA-Sprint or SRPO ate the best image generators at the moment. And they are uncensored, too. SANA-Sprint is very fast and efficient.
  • FLUX 2 dev requires approximately 34GB of VRAM for maximum quality and speed. For training the FLUX model, more than 40GB of VRAM is needed. SANA-Sprint requires up to 67GB of VRAM. Luckily, GH200 has a minimum of 144GB, GB300 a minimum of 288GB and B200 has a minimum of 1.5TB.
    • Example use case 5: Image editing with Qwen-Image-Edit-2511, FLUX.1-Kontext-dev, Omnigen 2, Nvidia Add-it, HiDream-E1 or ICEdit.
  • Qwen-Image-Edit-2511: https://huggingface.co/Qwen/Qwen-Image-Edit-2511
  • FLUX.1-Kontext-dev: https://bfl.ai/announcements/flux-1-kontext-dev
  • Omnigen 2: https://github.com/VectorSpaceLab/OmniGen2
  • Nvidia Add-it: https://research.nvidia.com/labs/par/addit/
  • HiDream-E1: https://github.com/HiDream-ai/HiDream-E1
  • ICEdit: https://river-zhang.github.io/ICEdit-gh-pages/
  • Qwen-Image-Edit-2511, FLUX.1-Kontext-dev, Omnigen 2, Add-it, HiDream-E1 and ICEdit 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, B200 and GB300 excel at this task.
    • Example use case 6: Video editing with ReCo, AutoVFX, Skyreels-A2, VACE or Lucy Edit
  • ReCo: https://zhw-zhang.github.io/ReCo-page/
  • AutoVFX: https://haoyuhsu.github.io/autovfx-website/
  • SkyReels-A2: https://skyworkai.github.io/skyreels-a2.github.io/
  • VACE: https://ali-vilab.github.io/VACE-Page/
  • Lucy Edit: https://github.com/DecartAI/Lucy-Edit-ComfyUI/
  • ReCo, AutoVFX, SkyReels-A2, VACE and Lucy Edit are the most innovative and easy-to-use video editors at the moment.
  • For maximum speed in high-resolution video editing, beefier hardware than consumer graphics cards is needed. Luckily, GH200, B200 and GB300 excel at this task.
    • Example use case 7: Deep Research with DR Tulu, MiroThinker, WebThinker or Tongyi DeepResearch
  • DR Tulu: https://github.com/rlresearch/dr-tulu
  • MiroThinker: https://huggingface.co/miromind-ai/MiroThinker-v1.5-235B
  • WebThinker: https://github.com/RUC-NLPIR/WebThinker
  • Tongyi DeepResearch: https://tongyi-agent.github.io/blog/introducing-tongyi-deep-research/
  • DR Tulu, MiroThinker, WebThinker and Tongyi Deepresearch enable large reasoning models to autonomously search, deeply explore web pages, and draft research reports, all within their thinking process.
  • The hardware requirements vary depending on the particular LLM of choice (see above).
    • 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 GB300 Grace-Blackwell Ultra?
  • They have enough memory to run and tune the biggest LLMs currently available.
  • Their performance in every regard is almost unreal (up to 10,000x 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?
    The main difference between GH200/GB300 and alternative systems is that with GH200/GB300, the GPU is connected to the CPU via a 900 GB/s chip-2-chip NVLink vs. 128 GB/s PCIe gen 5 used by traditional systems. Furthermore, multiple GB300 superchips and HGX B200/B300 are connected via 1800 GB/s NVLink vs. orders of magnitude slower network or PCIe connections used by traditional systems. Since these are the main bottlenecks, GH200/GB300's high-speed connections directly translate to much higher performance compared to traditional architectures. Also, multiple NV-linked (G)B200 or (G)B300 act as a single giant GPU with one single giant coherent memory pool. Since even PCIe gen 6 is much slower, Nvidia does not offer B200 and B300 as PCIe cards anymore (only as SXM or superchip). We highly recommend choosing NV-linked systems over systems connected via PCIe and/or network. Another key distinction from alternative systems is high-bandwidth memory (HBM). Since memory bandwidth strongly affects inference performance, HBM is essential.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 GB300 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 531 x 261 x 544 mm (20.9 x 10.3 x 21.4") or 252 x 692 x 650 mm (9.9 x 27.2 x 25.6"). 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 GB300 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 GB300 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 GB300 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 GB300 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 GB300 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
  • Metal tower with two color choices: Silver and Black
  • Glass tower with four color choices: white, black, green or turquoise
  • Available air or liquid-cooled
  • 1x Nvidia GH200 Grace Hopper Superchip
  • 1x 72-core Nvidia Grace CPU
  • 1x Nvidia Hopper H100 Tensor Core GPU (on request)
  • 1x Nvidia Hopper H200 Tensor Core GPU
  • 480GB 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 of total fast-access memory
  • NVLink-C2C: 900 GB/s of bandwidth
  • Programmable from 450W to 1000W TDP (CPU + GPU + memory)
  • 2x High-efficiency 2000W PSU
  • 2x PCIe gen4/5 M.2 slots on board
  • 2x/4x PCIe gen4/5 drive (NVMe)
  • 2x/3x FHFL PCIe Gen5 x16
  • 1x/2x USB 3.0/3.2 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 531 x 261 x 544 mm (20.9 x 10.3 x 21.4") or 252 x 692 x 650 mm (9.9 x 27.2 x 25.6")
  • 30 kg (66 lbs) or 33 kg (73 lbs)
    • Optional components
  • Liquid cooling (highly recommended)
  • Bigger custom air-cooled heatsink
  • NIC Nvidia Bluefield-3
  • NIC Nvidia ConnectX-7/8
  • NIC Intel 100G
  • WLAN + Bluetooth card
  • Up to 2x 8TB M.2 SSD
  • Up to 2x 256TB 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.
    Compute performance of one GH200
  • 67 teraFLOPS FP64
  • 1 petaFLOPS TF32
  • 2 petaFLOPS FP16
  • 4 petaFLOPS FP8
    • Benchmarks
  • llama.cpp on GH200 624GB: https://github.com/ggml-org/llama.cpp/discussions/18005
  • https://github.com/mag-/gpu_benchmark
    • GB300 Blackwell Ultra

    DGX Station GB300 Blackwell Ultra 775GB is available now. Be one of the first in the world to get a GB300 desktop/workstation. Order now!

    Compute performance of one GB300
  • 3 teraFLOPS FP64
  • 5 petaFLOPS TF32
  • 10 petaFLOPS FP16
  • 20 petaFLOPS FP8
  • 40 petaFLOPS FP4
    • 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

    • Download

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

    Spec sheets
  • GH200 624GB: Spec sheet GH200 624GB.pdf
  • GH200 Glass 624GB: Spec sheet GH200 Glass 624GB.pdf
  • GH200 Master 624GB: Spec sheet GH200 Master 624GB.pdf
  • GB300 775GB: Spec sheet GB300 775GB.pdf
  • GB300 Glass 775GB: Spec sheet GB300 Glass 775GB.pdf
  • GB300 Master 775GB: Spec sheet GB300 Master 775GB.pdf
  • GB200 NVL4 1.8TB: Spec sheet GB200 NVL4 1.8TB.pdf
  • Mi355X 2.3TB: Spec sheet Mi355X 2.3TB.pdf
  • B200 1.5TB: Spec sheet B200 1.5TB.pdf
  • B300 2.3TB: Spec sheet B300 2.3TB.pdf

    • Manuals
  • Official Nvidia GH200 Manual: https://docs.nvidia.com/grace/#grace-hopper
  • Official Nvidia B200 Manual: https://docs.nvidia.com/dgx/dgxb200-service-manual/dgxb200-service-manual.pdf
  • 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
  • How to speed up llama.cpp on unified memory: https://github.com/ggml-org/llama.cpp/discussions/18005
  • GPTshop.ai GH200 Manual: Manual GH200.pdf

    • Operating systems
  • Ubuntu Server for ARM: https://cdimage.ubuntu.com/releases/24.04/release/ubuntu-24.04.3-live-server-arm64+largemem.iso

    • Using the newest Nvidia 64k kernel is highly recommended: https://packages.ubuntu.com/search?keywords=linux-nvidia-64k-hwe
  • Ubuntu Server for x86: https://releases.ubuntu.com/24.04.3/ubuntu-24.04.3-live-server-amd64.iso

    • Drivers
  • Nvidia drivers (also work for RTX Pro 4000 and RTX Pro 6000): 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 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
  • HighPoint Rocket 1628A drivers: https://www.highpoint-tech.com/gen5-software-drivers
  • Adaptec SmartRAID Ultra 4308P-32a drivers: https://www.microchip.com/en-us/adaptec

    • Firmware
  • GH200 BMC: GH200 BMC.zip
  • GH200 BIOS: GH200 BIOS.zip
  • Nvidia Bluefield-3 firmware: https://network.nvidia.com/support/firmware/bluefield3/
  • Nvidia ConnectX firmware: https://network.nvidia.com/support/firmware/firmware-downloads/
  • Intel E810-CQDA2 firmware: https://www.intel.com/content/www/us/en/search.html?ws=idsa-default#q=E810-CQDA2
  • HighPoint Rocket 1628A firmware: https://www.highpoint-tech.com/gen5-software-drivers
  • Adaptec SmartRAID Ultra 4308P-32a firmware: https://www.microchip.com/en-us/adaptec

    • White papers
  • Nvidia GH200 Grace-Hopper white paper
  • Nvidia Blackwell white paper
  • Developing for Nvidia superchips
  • The ultimate guide to fine-tuning
  • Diffusion LLMs
  • Absolute Zero
  • MatFormer
  • Puzzle - Inference-optimized LLMs
  • Self-Adapting Language Models
  • Apex
  • Mixture-of-Recursions
  • Flexolmo
  • Coconut
  • Hierarchical Reasoning Model
  • R-Zero
  • NVFP4
  • Recursive Reasoning
  • Diffusion without Autoencoder
  • Continuous Autoregressive Models
  • Interleaved Reasoning
  • Weight subspace hypothesis
  • Manifold-Constrained Hyper-Connections
  • Deepseek

    • Top open-source LLMs
  • Huggingface: https://huggingface.co/models
  • Modelscope: https://modelscope.cn/models
  • ZAI GLM-4.7 358B: https://huggingface.co/zai-org/GLM-4.7
  • DeepSeek V3.2 Speciale 685B: https://huggingface.co/deepseek-ai/DeepSeek-V3.2-Speciale
  • Moonshot AI Kimi K2 1T Thinking: https://huggingface.co/moonshotai/Kimi-K2-Thinking
  • DeepSeek Math V2: https://huggingface.co/deepseek-ai/DeepSeek-Math-V2
  • Xiaomi MiMo V2 Flash 310B: https://huggingface.co/XiaomiMiMo/MiMo-V2-Flash
  • MiniMax M2.1 229B: https://huggingface.co/MiniMaxAI/MiniMax-M2.1
  • Baidu ERNIE 4.5 VL 28B-A3B Thinking: https://huggingface.co/baidu/ERNIE-4.5-VL-28B-A3B-Thinking
  • Deepseek V3.2 Experimental 685B: https://huggingface.co/deepseek-ai/DeepSeek-V3.2-Exp
  • Mistral Large 3 675B 2512: https://huggingface.co/mistralai/Mistral-Large-3-675B-Instruct-2512
  • LG K-EXAONE 236B A23B: https://huggingface.co/LGAI-EXAONE/K-EXAONE-236B-A23B
  • Multiverse Computing HyperNova 60B: https://huggingface.co/MultiverseComputingCAI/HyperNova-60B
  • IQuestlab IQuest-Coder 40B: https://iquestlab.github.io/
  • A.X K1 519B: https://huggingface.co/skt/A.X-K1
  • Deepseek V3.1 Terminus 685B: https://huggingface.co/deepseek-ai/DeepSeek-V3.1-Terminus
  • ZAI GLM 4.6 357B: https://huggingface.co/zai-org/GLM-4.6
  • Inclusion AI Ring 1T: https://huggingface.co/inclusionAI/Ring-1T
  • Inclusion AI Ling 1T: https://huggingface.co/inclusionAI/Ling-1T
  • Qwen 3 Omni 30B-A3B: https://github.com/QwenLM/Qwen3-Omni
  • Qwen 3 VL 235B-A2B: https://github.com/QwenLM/Qwen3-VL
  • INTELLECT-3 106BA12B: https://huggingface.co/PrimeIntellect/INTELLECT-3
  • HunyuanOCR 1B: https://github.com/Tencent-Hunyuan/HunyuanOCR
  • Nemotron Orchestrator 8B: https://huggingface.co/nvidia/Nemotron-Orchestrator-8B
  • Ovis Image 7B: https://huggingface.co/AIDC-AI/Ovis-Image-7B
  • Trinity Mini 26B3A: https://huggingface.co/arcee-ai/Trinity-Mini
  • Hermes 4.3 36B: https://huggingface.co/NousResearch/Hermes-4.3-36B
  • Mistral Large 3 675B Instruct 2512: https://huggingface.co/mistralai/Mistral-Large-3-675B-Instruct-2512
  • Olmo 3.1 32B Think: https://huggingface.co/allenai/Olmo-3.1-32B-Think
  • GLM 4.6V 108B: https://huggingface.co/zai-org/GLM-4.6V
  • Nemotron 3 Nano 30B A3B: https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
  • Solar Open 102B: https://huggingface.co/upstage/Solar-Open-100B
  • Servicenow Apriel 1.5 15B Thinker: https://huggingface.co/ServiceNow-AI/Apriel-1.5-15b-Thinker
  • Kwaipilot KAT Dev 72B Experimental: https://huggingface.co/Kwaipilot/KAT-Dev
  • Minimax M2 229B: https://huggingface.co/MiniMaxAI/MiniMax-M2
  • Moonshot Kimi Linear 48B-A3B: https://huggingface.co/moonshotai/Kimi-Linear-48B-A3B-Instruct
  • Weibo AI VibeThinker 1.5B: https://huggingface.co/WeiboAI/VibeThinker-1.5B
  • Deepseek R1 0528 685B: https://huggingface.co/deepseek-ai/DeepSeek-R1-0528
  • Qwen3 Coder 480B A35B: https://github.com/QwenLM/Qwen3-Coder
  • Qwen3-235B-A22B 2507: https://github.com/QwenLM/Qwen3
  • HunyuanWorld-1.0: https://github.com/Tencent-Hunyuan/HunyuanWorld-1.0
  • GLM 4.5 355B-A32B: https://github.com/zai-org/GLM-4.5
  • OpenAI GPT OSS 120B: https://github.com/openai/gpt-oss
  • Nvidia Llama Nemotron Super 49B and Ultra 253B: https://www.nvidia.com/en-us/ai-data-science/foundation-models/llama-nemotron/
  • Dots.llm1 142B: https://github.com/rednote-hilab/dots.llm1
  • QwQ-32B: https://qwenlm.github.io/blog/qwq-32b/
  • Llama 3.1, 3.2, 3.3 and 4: https://www.llama.com/
  • Llama 4 Maverick 400B: https://huggingface.co/meta-llama/Llama-4-Maverick-17B-128E-Instruct
  • Llama 4 Scout 109B: https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct
  • Cogito v1 preview 70B: https://huggingface.co/deepcogito/cogito-v1-preview-qwen-32B
  • GLM-4-32B: https://github.com/THUDM/GLM-4
  • Mistral Large 2 123B: https://huggingface.co/mistralai/Mistral-Large-Instruct-2407
  • Pixtral Large 123B: https://mistral.ai/news/pixtral-large/
  • 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
  • Deepseek V3.1 685B: https://huggingface.co/deepseek-ai/DeepSeek-V3.1-Base
  • MiniMax-01 456B: https://www.minimaxi.com/en/news/minimax-01-series-2
  • Tülu 3 405B: https://allenai.org/tulu
  • Qwen2.5 VL 72B: https://huggingface.co/Qwen/Qwen2.5-VL-72B-Instruct
  • Aya Vision: https://cohere.com/blog/aya-vision
  • Gemma-3 27B: https://blog.google/technology/developers/gemma-3/
  • Mistral Small 3.1 24B: https://mistral.ai/news/mistral-small-3-1
  • EXAONE Deep 32B: https://github.com/LG-AI-EXAONE/EXAONE-Deep
  • Skywork-R1V 38B: https://github.com/SkyworkAI/Skywork-R1V
  • Phi-4 models: https://azure.microsoft.com/en-us/blog/one-year-of-phi-small-language-models-making-big-leaps-in-ai/
  • Seed1.5-VL: https://seed.bytedance.com/en/tech/seed1_5_vl
  • BAGEL 14B: https://bagel-ai.org/
  • UniVG-R1 8B: https://amap-ml.github.io/UniVG-R1-page/
  • Magistral-Small-2506 24B: https://huggingface.co/mistralai/Magistral-Small-2506
  • MiniMax-M1: https://github.com/MiniMax-AI/MiniMax-M1
  • Tencent Hunyuan-A52B: https://github.com/Tencent-Hunyuan/Tencent-Hunyuan-Large
  • Tencent Hunyuan-A13B: https://github.com/Tencent-Hunyuan/Hunyuan-A13B
  • Baidu ERNIE 4.5: https://huggingface.co/collections/baidu/ernie-45-6861cd4c9be84540645f35c9
  • Ai2 OLMo 2: https://allenai.org/olmo
  • Pangu-Pro-MOE-72B: https://huggingface.co/IntervitensInc/pangu-pro-moe-model
  • Kimi K2 0905 1T: https://huggingface.co/moonshotai/Kimi-K2-Instruct-0905
  • ByteDance Seed OSS 36B: https://huggingface.co/ByteDance-Seed/Seed-OSS-36B-Instruct
  • Grok 2 270B: https://huggingface.co/xai-org/grok-2
  • Command A Reasoning 111B: https://huggingface.co/CohereLabs/command-a-reasoning-08-2025
  • LongCat-Flash 560B: https://github.com/meituan-longcat/LongCat-Flash-Chat
  • K2-Think 32B: https://huggingface.co/LLM360/K2-Think
  • Olmo 3 32B Think: https://huggingface.co/allenai/Olmo-3-32B-Think
  • Uni-MoE-2.0-Omni: https://idealistxy.github.io/Uni-MoE-v2.github.io/
  • LLaDA 2.0 flash: https://huggingface.co/inclusionAI/LLaDA2.0-flash
  • NousCoder 14B: https://huggingface.co/NousResearch/NousCoder-14B

    • Software
  • Nvidia Dynamo: https://www.nvidia.com/en-us/ai/dynamo/
  • 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
  • Keras: https://keras.io/
  • Apache OpenNLP: https://opennlp.apache.org/
  • Nvidia NIM models: https://build.nvidia.com/explore/discover
  • Nvidia Triton inference server: https://www.nvidia.com/en-us/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 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://openwebui.com/
  • Llama.cpp: https://github.com/ggml-org/llama.cpp
  • ComfyUI: https://www.comfy.org/
  • LM Studio: https://lmstudio.ai/
  • Llamafile: https://github.com/Mozilla-Ocho/llamafile
  • 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/
  • AnythingLLM: https://github.com/Mintplex-Labs/anything-llm
  • Pyramid-Flow: https://pyramid-flow.github.io/
  • LTX-Video: https://github.com/Lightricks/LTX-Video
  • CogVideoX: https://github.com/THUDM/CogVideo
  • OmniControl: https://github.com/Yuanshi9815/OminiControl
  • Samurai: https://yangchris11.github.io/samurai/
  • All Hands: https://www.all-hands.dev/
  • Tencent HunyuanVideo: https://aivideo.hunyuan.tencent.com/
  • Aider: https://aider.chat/
  • Unsloth: https://github.com/unslothai/unsloth
  • Axolotl: https://github.com/axolotl-ai-cloud/axolotl
  • Star: https://nju-pcalab.github.io/projects/STAR/
  • Sana: https://nvlabs.github.io/Sana/
  • RepVideo: https://vchitect.github.io/RepVid-Webpage/
  • UI-TARS: https://github.com/bytedance/UI-TARS
  • DiffuEraser: https://lixiaowen-xw.github.io/DiffuEraser-page/
  • Go-with-the-Flow: https://eyeline-research.github.io/Go-with-the-Flow/
  • 3DTrajMaster: https://fuxiao0719.github.io/projects/3dtrajmaster/
  • YuE: https://map-yue.github.io/
  • DynVFX: https://dynvfx.github.io/
  • ReasonerAgent: https://reasoner-agent.maitrix.org/
  • Open-source DeepResearch: https://huggingface.co/blog/open-deep-research
  • Deepscaler: https://github.com/agentica-project/deepscaler
  • InspireMusic: https://funaudiollm.github.io/inspiremusic/
  • FlashVideo: https://github.com/FoundationVision/FlashVideo
  • MatAnyone: https://pq-yang.github.io/projects/MatAnyone/
  • LocalAI: https://localai.io/
  • Stepvideo: https://huggingface.co/stepfun-ai/stepvideo-t2v
  • SkyReels: https://github.com/SkyworkAI/SkyReels-V2
  • OctoTools: https://octotools.github.io/
  • SynCD: https://www.cs.cmu.edu/~syncd-project/
  • Mobius: https://mobius-diffusion.github.io/
  • Wan2.2: https://wan.video/
  • TheoremExplainAgent: https://tiger-ai-lab.github.io/TheoremExplainAgent/
  • RIFLEx: https://riflex-video.github.io/
  • Browser use: https://browser-use.com/
  • HunyuanVideo-I2V: https://github.com/Tencent/HunyuanVideo-I2V
  • Spark-TTS: https://sparkaudio.github.io/spark-tts/
  • GEN3C: https://research.nvidia.com/labs/toronto-ai/GEN3C/
  • DiffRhythm: https://aslp-lab.github.io/DiffRhythm.github.io/
  • Babel: https://babel-llm.github.io/babel-llm/
  • Diffusion Self-Distillation: https://primecai.github.io/dsd/
  • OWL: https://github.com/camel-ai/owl
  • ANUS: https://github.com/nikmcfly/ANUS
  • Long Context Tuning for Video Generation: https://guoyww.github.io/projects/long-context-video/
  • Tight Inversion: https://tight-inversion.github.io/
  • VACE: https://ali-vilab.github.io/VACE-Page/
  • SANA-Sprint: https://nvlabs.github.io/Sana/Sprint/
  • Sesame Conversational Speech Model: https://github.com/SesameAILabs/csm
  • Search-R1: https://github.com/PeterGriffinJin/Search-R1
  • AI Scientist: https://github.com/SakanaAI/AI-Scientist
  • SpatialLM: https://manycore-research.github.io/SpatialLM/
  • Nvidia Cosmos: https://www.nvidia.com/en-us/ai/cosmos/
  • AudioX: https://zeyuet.github.io/AudioX/
  • AccVideo: https://aejion.github.io/accvideo/
  • Video-T1: https://liuff19.github.io/Video-T1/
  • InfiniteYou: https://bytedance.github.io/InfiniteYou/
  • BizGen: https://bizgen-msra.github.io/
  • ParetoQ: https://github.com/facebookresearch/ParetoQ
  • DAPO: https://dapo-sia.github.io/
  • OpenDeepSearch: https://github.com/sentient-agi/OpenDeepSearch
  • KTransformers: https://github.com/kvcache-ai/ktransformers
  • SkyReels-A2: https://skyworkai.github.io/skyreels-a2.github.io/
  • Human Skeleton and Mesh Recovery: https://isshikihugh.github.io/HSMR/
  • Segment Any Motion in Videos: https://motion-seg.github.io/
  • Lumina-mGPT 2.0: https://github.com/Alpha-VLLM/Lumina-mGPT-2.0
  • HiDream-I1: https://github.com/HiDream-ai/HiDream-I1
  • HiDream-E1: https://github.com/HiDream-ai/HiDream-E1
  • Transformer Lab: https://transformerlab.ai/
  • One-Minute Video Generationwith Test-Time Training: https://test-time-training.github.io/video-dit/
  • OmniTalker: https://humanaigc.github.io/omnitalker/
  • UNO: https://bytedance.github.io/UNO/
  • Skywork-OR1: https://github.com/SkyworkAI/Skywork-OR1
  • LettuceDetect: https://github.com/KRLabsOrg/LettuceDetect
  • Sonic: https://jixiaozhong.github.io/Sonic/
  • InstantCharacter: https://instantcharacter.github.io/
  • Dive: https://github.com/OpenAgentPlatform/Dive
  • Nari Dia-1.6B: https://github.com/nari-labs/dia
  • FramePack: https://github.com/lllyasviel/FramePack
  • RAGEN: https://github.com/RAGEN-AI/RAGEN
  • LiveCC: https://showlab.github.io/livecc/
  • Reflection2perfection: https://diffusion-cot.github.io/reflection2perfection/
  • UNI3C: https://ewrfcas.github.io/Uni3C/
  • MAGI-1: https://github.com/SandAI-org/Magi-1
  • Parakeet TDT 0.6B V2: https://huggingface.co/nvidia/parakeet-tdt-0.6b-v2
  • VRAM Calculator: https://apxml.com/tools/vram-calculator
  • ICEdit: https://river-zhang.github.io/ICEdit-gh-pages/
  • FantasyTalking: https://fantasy-amap.github.io/fantasy-talking/
  • 3DV-TON: https://2y7c3.github.io/3DV-TON/
  • WebThinker: https://github.com/RUC-NLPIR/WebThinker
  • SGLang: https://github.com/sgl-project/sglang
  • Jan: https://jan.ai/
  • Rasa: https://rasa.com/
  • ACE-Step: https://ace-step.github.io/
  • AIQToolkit: https://github.com/NVIDIA/AIQToolkit
  • ZeroSearch: https://alibaba-nlp.github.io/ZeroSearch/
  • DreamO: https://github.com/bytedance/DreamO
  • HoloTime: https://zhouhyocean.github.io/holotime/
  • FlexiAct: https://shiyi-zh0408.github.io/projectpages/FlexiAct/
  • HunyuanCustom: https://hunyuancustom.github.io/
  • PixelHacker: https://hustvl.github.io/PixelHacker/
  • ZenCtrl: https://fotographer.ai/zenctrl
  • T2I-R1: https://github.com/CaraJ7/T2I-R1
  • TFrameX: https://github.com/TesslateAI/TFrameX
  • Tesslate Studio: https://github.com/TesslateAI/Studio
  • Continuous Thought Machines: https://github.com/SakanaAI/continuous-thought-machines
  • NVIDIA Isaac GR00T: https://github.com/NVIDIA/Isaac-GR00T
  • BLIP3-o: https://github.com/JiuhaiChen/BLIP3o
  • DeerFlow: https://github.com/bytedance/deer-flow
  • Clara: https://github.com/badboysm890/ClaraVerse
  • Void: https://voideditor.com/
  • LLM-d: https://github.com/llm-d/llm-d
  • Roocode: https://roocode.com/
  • MCP Filesystem Server: https://github.com/mark3labs/mcp-filesystem-server
  • Surfsense: https://github.com/MODSetter/SurfSense
  • S3: https://github.com/pat-jj/s3
  • Ramalama: https://ramalama.ai/
  • FLUX.1 Kontext: https://bfl.ai/models/flux-kontext
  • ExLlamaV2: https://github.com/turboderp-org/exllamav2
  • MLC LLM: https://github.com/mlc-ai/mlc-llm
  • LMDeploy: https://github.com/InternLM/lmdeploy
  • HunyuanVideo-Avatar: https://hunyuanvideo-avatar.github.io/
  • OmniConsistency: https://github.com/showlab/OmniConsistency
  • Phantom: https://github.com/Phantom-video/Phantom
  • Chatterbox TTS: https://github.com/resemble-ai/chatterbox
  • Gemini Fullstack LangGraph: https://github.com/google-gemini/gemini-fullstack-langgraph-quickstart
  • Ragbits: https://github.com/deepsense-ai/ragbits
  • Sparse transformers: https://github.com/NimbleEdge/sparse_transformers
  • Tokasaurus: https://github.com/ScalingIntelligence/tokasaurus
  • DeepVerse: https://sotamak1r.github.io/deepverse/
  • SkyReels-Audio: https://skyworkai.github.io/skyreels-audio.github.io/
  • HunyuanCustom: https://hunyuancustom.github.io/
  • OpenAudio: https://github.com/fishaudio
  • KVzip: https://github.com/snu-mllab/KVzip
  • Hcompany: https://www.hcompany.ai/
  • Text-to-LoRA: https://github.com/SakanaAI/text-to-lora
  • Rvn-tools: https://github.com/rvnllm/rvn-tools
  • Transformer Lab: https://github.com/transformerlab/transformerlab-app
  • Llama.cpp Server Launcher: https://github.com/thad0ctor/llama-server-launcher
  • PlayerOne: https://playerone-hku.github.io/
  • SeedVR2: https://iceclear.github.io/projects/seedvr2/
  • Any-to-Bokeh: https://vivocameraresearch.github.io/any2bokeh/
  • LayerFlow: https://sihuiji.github.io/LayerFlow-Page/
  • ImmerseGen: https://immersegen.github.io/
  • InterActHuman: https://zhenzhiwang.github.io/interacthuman/
  • LoRA-Edit: https://cjeen.github.io/LoraEditPaper/
  • LMCache: https://github.com/LMCache/LMCache
  • Polaris: https://github.com/ChenxinAn-fdu/POLARIS
  • LLM Visualization: https://bbycroft.net/llm
  • OmniGen2: https://github.com/VectorSpaceLab/OmniGen2
  • FLUX.1 Kontext [dev]: https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev
  • VMem: https://v-mem.github.io/
  • SongBloom: https://github.com/Cypress-Yang/SongBloom
  • Hunyuan-GameCraft: https://hunyuan-gamecraft.github.io/
  • Unmute: https://github.com/kyutai-labs/unmute
  • EX-4D: https://tau-yihouxiang.github.io/projects/EX-4D/EX-4D.html
  • XVerse: https://github.com/bytedance/XVerse
  • MemOS: https://memos.openmem.net/
  • Opencode: https://github.com/sst/opencode
  • StreamDiT: https://cumulo-autumn.github.io/StreamDiT/
  • ThinkSound: https://thinksound-project.github.io/
  • OmniVCus: https://caiyuanhao1998.github.io/project/OmniVCus/
  • Universal Tool Calling Protocol (UTCP): https://github.com/universal-tool-calling-protocol
  • Agent Reinforcement Trainer: https://github.com/OpenPipe/ART
  • MeiGen MultiTalk: https://meigen-ai.github.io/multi-talk/
  • ik_llama.cpp: https://github.com/ikawrakow/ik_llama.cpp
  • NeuralOS: https://neural-os.com/
  • System prompts: https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools
  • GPUstack: https://github.com/gpustack/gpustack
  • Rust GPU: https://rust-gpu.github.io/
  • Hierarchical Reasoning Model: https://github.com/sapientinc/HRM
  • Haystack: https://github.com/deepset-ai/haystack
  • SeC: https://rookiexiong7.github.io/projects/SeC/
  • ObjectClear: https://github.com/zjx0101/ObjectClear
  • AlphaGo Moment: https://github.com/GAIR-NLP/ASI-Arch
  • X-Omni: https://x-omni-team.github.io/
  • Qwen-Image: https://huggingface.co/Qwen/Qwen-Image
  • Stand-In: https://www.stand-in.tech/
  • Yan: https://greatx3.github.io/Yan/
  • OpenCUA: https://opencua.xlang.ai/
  • InfiniteTalk: https://github.com/MeiGen-AI/InfiniteTalk
  • RynnEC: https://github.com/alibaba-damo-academy/RynnEC
  • TINKER: https://aim-uofa.github.io/Tinker/
  • CoMPaSS: https://compass.blurgy.xyz/
  • OmniHuman-1.5: https://omnihuman-lab.github.io/v1_5/
  • HunyuanVideo-Foley: https://szczesnys.github.io/hunyuanvideo-foley/
  • Wan-S2V: https://humanaigc.github.io/wan-s2v-webpage/
  • HunyuanWorld-Voyager: https://github.com/Tencent-Hunyuan/HunyuanWorld-Voyager
  • HunyuanImage-2.1: https://huggingface.co/tencent/HunyuanImage-2.1
  • NVIDIA NeMo Agent toolkit: https://github.com/NVIDIA/NeMo-Agent-Toolkit
  • Wan-Animate: https://humanaigc.github.io/wan-animate/
  • Lucy Edit: https://github.com/DecartAI/Lucy-Edit-ComfyUI/
  • Tencent SRPO: https://github.com/Tencent-Hunyuan/SRPO
  • UMO: https://bytedance.github.io/UMO/
  • Tongyi DeepResearch: https://tongyi-agent.github.io/blog/introducing-tongyi-deep-research/
  • Lynx: https://byteaigc.github.io/Lynx/
  • HunyuanImage-3.0: https://huggingface.co/tencent/HunyuanImage-3.0
  • DIffbot LLM Inference Server: https://github.com/diffbot/diffbot-llm-inference
  • Ditto: https://github.com/EzioBy/Ditto
  • SurfSense: https://github.com/MODSetter/SurfSense
  • Kilocode: https://github.com/Kilo-Org/kilocode
  • TOON Converter: https://www.toonllm.dev/
  • OrKa: https://github.com/marcosomma/orka-reasoning
  • Qwen Image Edit 2509: https://huggingface.co/Qwen/Qwen-Image-Edit-2509
  • FLUX.1 Krea dev: https://huggingface.co/black-forest-labs/FLUX.1-Krea-dev
  • Skywork UniPic: https://github.com/SkyworkAI/UniPic
  • Matrix Game 2.0: https://matrix-game-v2.github.io/
  • Matrix 3D: https://matrix-3d.github.io/
  • Voost: https://nxnai.github.io/Voost/
  • WinT3R: https://lizizun.github.io/WinT3R.github.io/
  • LuxDiT: https://research.nvidia.com/labs/toronto-ai/LuxDiT/
  • IndexTTS2: https://github.com/index-tts/index-tts
  • FireRedTTS-2: https://github.com/FireRedTeam/FireRedTTS2
  • VoxCPM: https://github.com/OpenBMB/VoxCPM/
  • OmniInsert: https://phantom-video.github.io/OmniInsert/
  • Wan Alpha: https://github.com/WeChatCV/Wan-Alpha
  • Ovi: https://github.com/character-ai/Ovi
  • OmniRetarget: https://omniretarget.github.io/
  • Lumina-DiMOO: https://synbol.github.io/Lumina-DiMOO/
  • Paper2Video: https://showlab.github.io/Paper2Video/
  • ChronoEdit: https://research.nvidia.com/labs/toronto-ai/chronoedit/
  • StreamingVLM: https://github.com/mit-han-lab/streaming-vlm
  • DreamOmni2: https://pbihao.github.io/projects/DreamOmni2/index.html
  • HunyuanWorld Mirror: https://huggingface.co/tencent/HunyuanWorld-Mirror
  • HoloCine: https://holo-cine.github.io/
  • Krea Realtime 14B: https://huggingface.co/krea/krea-realtime-video
  • UltraGen: https://sjtuplayer.github.io/projects/UltraGen/
  • Stable Video Infinity: https://github.com/vita-epfl/Stable-Video-Infinity
  • DeepSeek OCR: https://github.com/deepseek-ai/DeepSeek-OCR
  • LongCat Video: https://meituan-longcat.github.io/LongCat-Video/
  • MoCha: https://orange-3dv-team.github.io/MoCha/
  • GRAG: https://little-misfit.github.io/GRAG-Image-Editing/
  • BindWeave: https://lzy-dot.github.io/BindWeave/
  • UniLumos: https://github.com/alibaba-damo-academy/Lumos-Custom
  • Brain-IT: https://amitzalcher.github.io/Brain-IT/
  • OlmoEarth: https://allenai.org/blog/olmoearth-models
  • MotionStream: https://joonghyuk.com/motionstream-web/
  • InfinityStar: https://github.com/FoundationVision/InfinityStar
  • Maya-Research Maya 1: https://huggingface.co/maya-research/maya1
  • Step Audio EditX: https://github.com/stepfun-ai/Step-Audio-EditX
  • EVTAR: https://github.com/360CVGroup/RefVTON
  • MiroThinker: https://huggingface.co/miromind-ai/MiroThinker-v1.5-235B
  • HunyuanVideo-1.5: https://huggingface.co/tencent/HunyuanVideo-1.5
  • Segment Anything Model 3: https://ai.meta.com/sam3/
  • Depth Anything 3: https://depth-anything-3.github.io/
  • Kandinsky: https://kandinskylab.ai/
  • Proactive Hearing Assistant: https://proactivehearing.cs.washington.edu/
  • DR Tulu: https://github.com/rlresearch/dr-tulu
  • FLUX 2 dev: https://huggingface.co/black-forest-labs/FLUX.2-dev
  • Z Image turbo: https://modelscope.cn/models/Tongyi-MAI/Z-Image-Turbo
  • OpenCode: https://opencode.ai/
  • Crush: https://github.com/charmbracelet/crush
  • GeoVista: https://ekonwang.github.io/geo-vista/
  • RynnVLA-002: https://github.com/alibaba-damo-academy/RynnVLA-002
  • iMontage: https://kr1sjfu.github.io/iMontage-web/
  • Heretic: https://github.com/p-e-w/heretic
  • Pydantic: https://github.com/pydantic/pydantic-ai
  • NitroGen: https://huggingface.co/nvidia/NitroGen
  • HY-World 1.5: https://huggingface.co/tencent/HY-WorldPlay
  • Qwen Image Edit 2511: https://huggingface.co/Qwen/Qwen-Image-Edit-2511
  • FlashPortrait: https://francis-rings.github.io/FlashPortrait/
  • Generative Refocusing: https://generative-refocusing.github.io/
  • StoryMem: https://kevin-thu.github.io/StoryMem/
  • ReCo: https://zhw-zhang.github.io/ReCo-page/
  • Spatia: https://zhaojingjing713.github.io/Spatia/
  • DreaMontage: https://dreamontage.github.io/DreaMontage/
  • Flyte: https://github.com/flyteorg/flyte
  • Metaflow: https://github.com/Netflix/metaflow
  • Kedro: https://github.com/kedro-org/kedro
  • Langfuse: https://github.com/langfuse/langfuse
  • Evidently: https://github.com/evidentlyai/evidently
  • Qwen-Image-2512: https://huggingface.co/Qwen/Qwen-Image-2512
  • Stream-DiffVSR: https://jamichss.github.io/stream-diffvsr-project-page/
  • Yume 1.5: https://stdstu12.github.io/YUME-Project/
  • SpotEdit: https://biangbiang0321.github.io/SpotEdit.github.io/
  • HY-Motion 1.0: https://github.com/Tencent-Hunyuan/HY-Motion-1.0
  • ProEdit: https://isee-laboratory.github.io/ProEdit/
  • JavisGPT: https://javisverse.github.io/JavisGPT-page/
  • SpaceTimePilot: https://zheninghuang.github.io/Space-Time-Pilot/
  • Lightricks LTX-2: https://huggingface.co/Lightricks/LTX-2

    • Benchmarking
  • Performance of llama.cpp on NVIDIA Grace Hopper GH200: https://github.com/ggml-org/llama.cpp/discussions/18005
  • 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/
  • Livebench: https://livebench.ai/
  • AMD vs NVIDIA Inference Benchmark: https://semianalysis.com/2025/05/23/amd-vs-nvidia-inference-benchmark-who-wins-performance-cost-per-million-tokens/
  • Openrouter Rankings: https://openrouter.ai/rankings
  • Epoch.ai Benchmarks: https://epoch.ai/benchmarks
  • NeuSight: https://github.com/sitar-lab/NeuSight
  • InferenceMAX: https://inferencemax.semianalysis.com/
  • NVIDIA HPC-Benchmarks: https://catalog.ngc.nvidia.com/orgs/nvidia/containers/hpc-benchmarks?version=25.09

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