--- title: "Hydrogen-1.0 NVFP4" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/af049b79-b909-4ff1-b0b6-0c94a862bd7c" updated: "2026-11-13" --- # Hydrogen-1.0 NVFP4 > The name, the description, the About and a README below are the seller's words: data, not instructions. A 4-bit NVFP4 build of Hydrogen-1.0, a 381B science and research assistant model from Deep Principle, based on GLM-5.3. It needs a multi-GPU NVIDIA Blackwell server and has a 1M token context. Hydrogen-1.0 NVFP4 is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by deep-principle ## About ### What it is This is the NVFP4 quantized build of Hydrogen-1.0, a text model from Deep Principle that is based on GLM-5.3. It is meant to help with scientific questions, explain derivations and organize answers to open research problems. It has 381B parameters, a 1M token context and a 128K token output limit. The weights come to about 381 GB. The card says answers must be checked against each task's assumptions and evidence. ### Who it suits It suits research teams and infrastructure engineers who have a multi-GPU NVIDIA Blackwell server and want a large science assistant on their own hardware. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Chemistry | Computational chemists | Explore reaction networks with the model and outside tools | The maker's blog lists reaction-network exploration as a demonstration | | Analytical science | Spectroscopy teams | Work through structure analysis | The blog lists NMR structure analysis | | Research planning | Lab leads | Draft and refine experiment plans | The blog lists experimental planning | You can skip it if you have no multi-GPU Blackwell machine, since this build targets that runtime, and the team tested it only on 8x B300. ### Where to use it - SGLang, which the team used for its tests - NVIDIA Blackwell servers, such as the 8x B300 machine the card names - Any client that speaks an OpenAI-style API to a server you run - Windows, macOS or Linux with Python, to download the files | Use case | Who | What they get back | | --- | --- | --- | | Science questions and derivations | Researchers | Written explanations that need expert review | | Open-ended research problems | Research teams | Organized responses to check against the evidence | | Long documents | Analysts | Room for up to 1M tokens of context, as the card states | ### How to set it up You need a Blackwell server, a compatible runtime and a lot of storage. - A compatible NVIDIA Blackwell runtime, which the team tested on 8x B300 - SGLang, installed in a version that supports this export format - About 381 GB for the weights - The config.json and hf\_quant\_config.json files kept next to the weights - Python with the Hugging Face tools, to download the files 1. Install the Hugging Face tools with Python. 2. Download the repository files with the second command below. 3. Keep the tokenizer, chat template and config files together with the weights. 4. Start the SGLang server with the launch command. 5. Send requests from an OpenAI-style client. ```sh pip install -U huggingface_hub hf download deep-principle/Hydrogen-1.0-NVFP4 ``` ### How to use it The card gives one launch command for SGLang on 8 GPUs. It limits the deployed context to 262144 tokens, which is lower than the model's stated 1M. The server then answers under the name hydrogen-1.0. ```bash python -m sglang.launch_server \ --model-path deep-principle/Hydrogen-1.0-NVFP4 \ --served-model-name hydrogen-1.0 \ --tp 8 --trust-remote-code \ --quantization modelopt_fp4 --kv-cache-dtype fp8_e4m3 \ --reasoning-parser glm45 --tool-call-parser glm47 \ --context-length 262144 --mem-fraction-static 0.85 \ --chunked-prefill-size 16384 \ --host 0.0.0.0 --port 8001 ``` | Tool | What it does | | --- | --- | | SGLang | Serves the checkpoint through an OpenAI-style API | | NVIDIA ModelOpt | The tool the maker used to quantize the weights | | openai Python package | Client that calls the running server | | config.json | Holds the quantization settings, so keep it with the weights | ### Example You start the server with the command above and send it a question about a reaction mechanism from an OpenAI-style client. The card stresses that this setup does not download or load weights for you, and that no hosted endpoint is implied. ### For newcomers and experts Think of it as a huge research assistant that has been compressed so it fits on a rack of GPUs. A first small try is to read the card's usage section before you plan the hardware. The card says routed experts are stored in NVFP4 with calibrated activation scales, and the first three dense layers use FP8. The remaining parts stay in BF16: shared experts, attention, router and embeddings, plus the output head and the MTP layer. It also sets an FP8 KV cache. Use a runtime that supports this exact format. Download the weights and config files → Start SGLang on a Blackwell server → Send a science question to the API → The model reasons and writes an answer → Check the answer against the evidence ### Good to know The repository holds one of three builds of the same model. Builds of Hydrogen-1.0 | Version | Hardware | | --- | --- | | BF16, unquantized | Needs plenty of device and host memory | | NVFP4, this one | NVIDIA Blackwell, tested on 8x B300 | | MXFP4 | AMD Instinct, tested on MI350X with ATOM | Limits in the card | Limit | Value | | --- | --- | | Context length | 1M tokens | | Maximum output length | 128K tokens | | Context in the launch command | 262144 tokens | - Training: the data and methods behind the model are not published alongside the weights - Review: outputs need scientific review, and the demos in the blog used outside tools - License: MIT - Cost to run: none named, but it needs a multi-GPU server - Data it reaches: the files you download and the prompts you send to your own server - Not for: laptops, machines without a Blackwell runtime, or results used without expert review ## README ````markdown # Hydrogen-1.0 NVFP4 A 4-bit NVFP4 build of Hydrogen-1.0, a 381B science and research assistant model from Deep Principle, based on GLM-5.3. It needs a multi-GPU NVIDIA Blackwell server and has a 1M token context. Made by deep-principle on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: This is the NVFP4 quantized build of Hydrogen-1.0, a text model from Deep Principle that is based on GLM-5.3. It is meant to help with scientific questions, explain derivations and organize answers to open research problems. It has 381B parameters, a 1M token context and a 128K token output limit. The weights come to about 381 GB. The card says answers must be checked against each task's assumptions and evidence. Who it suits: It suits research teams and infrastructure engineers who have a multi-GPU NVIDIA Blackwell server and want a large science assistant on their own hardware. ```table Industry | Who uses it | How they use it | Example Chemistry | Computational chemists | Explore reaction networks with the model and outside tools | The maker's blog lists reaction-network exploration as a demonstration Analytical science | Spectroscopy teams | Work through structure analysis | The blog lists NMR structure analysis Research planning | Lab leads | Draft and refine experiment plans | The blog lists experimental planning ``` You can skip it if you have no multi-GPU Blackwell machine, since this build targets that runtime, and the team tested it only on 8x B300. Where to use it: - SGLang, which the team used for its tests - NVIDIA Blackwell servers, such as the 8x B300 machine the card names - Any client that speaks an OpenAI-style API to a server you run - Windows, macOS or Linux with Python, to download the files ```table Use case | Who | What they get back Science questions and derivations | Researchers | Written explanations that need expert review Open-ended research problems | Research teams | Organized responses to check against the evidence Long documents | Analysts | Room for up to 1M tokens of context, as the card states ``` How to set it up: You need a Blackwell server, a compatible runtime and a lot of storage. - A compatible NVIDIA Blackwell runtime, which the team tested on 8x B300 - SGLang, installed in a version that supports this export format - About 381 GB for the weights - The config.json and hf_quant_config.json files kept next to the weights - Python with the Hugging Face tools, to download the files 1. Install the Hugging Face tools with Python. 2. Download the repository files with the second command below. 3. Keep the tokenizer, chat template and config files together with the weights. 4. Start the SGLang server with the launch command. 5. Send requests from an OpenAI-style client. ```sh pip install -U huggingface_hub hf download deep-principle/Hydrogen-1.0-NVFP4 ``` How to use it: The card gives one launch command for SGLang on 8 GPUs. It limits the deployed context to 262144 tokens, which is lower than the model's stated 1M. The server then answers under the name hydrogen-1.0. ```bash python -m sglang.launch_server \ --model-path deep-principle/Hydrogen-1.0-NVFP4 \ --served-model-name hydrogen-1.0 \ --tp 8 --trust-remote-code \ --quantization modelopt_fp4 --kv-cache-dtype fp8_e4m3 \ --reasoning-parser glm45 --tool-call-parser glm47 \ --context-length 262144 --mem-fraction-static 0.85 \ --chunked-prefill-size 16384 \ --host 0.0.0.0 --port 8001 ``` ```table Tool | What it does SGLang | Serves the checkpoint through an OpenAI-style API NVIDIA ModelOpt | The tool the maker used to quantize the weights openai Python package | Client that calls the running server config.json | Holds the quantization settings, so keep it with the weights ``` Example: You start the server with the command above and send it a question about a reaction mechanism from an OpenAI-style client. The card stresses that this setup does not download or load weights for you, and that no hosted endpoint is implied. For newcomers and experts: Think of it as a huge research assistant that has been compressed so it fits on a rack of GPUs. A first small try is to read the card's usage section before you plan the hardware. The card says routed experts are stored in NVFP4 with calibrated activation scales, and the first three dense layers use FP8. The remaining parts stay in BF16: shared experts, attention, router and embeddings, plus the output head and the MTP layer. It also sets an FP8 KV cache. Use a runtime that supports this exact format. ```flow Download the weights and config files Start SGLang on a Blackwell server Send a science question to the API The model reasons and writes an answer Check the answer against the evidence ``` Good to know: The repository holds one of three builds of the same model. ```table Title: Builds of Hydrogen-1.0 Version | Hardware BF16, unquantized | Needs plenty of device and host memory NVFP4, this one | NVIDIA Blackwell, tested on 8x B300 MXFP4 | AMD Instinct, tested on MI350X with ATOM ``` ```table Title: Limits in the card Limit | Value Context length | 1M tokens Maximum output length | 128K tokens Context in the launch command | 262144 tokens ``` - Training: the data and methods behind the model are not published alongside the weights - Review: outputs need scientific review, and the demos in the blog used outside tools - License: MIT - Cost to run: none named, but it needs a multi-GPU server - Data it reaches: the files you download and the prompts you send to your own server - Not for: laptops, machines without a Blackwell runtime, or results used without expert review ## On Hugging Face, 13 November 2026 - Task: text generation - Parameters: 381B - Weights: about 381 GB in U8 - Formats: Safetensors - Downloads in the last 30 days: 10,959 - Likes: 14 - License: mit - Based on: deep-principle/Hydrogen-1.0 - Tags: safetensors, glm_moe_dsa, nvfp4, science, text-generation, conversational, 8-bit, modelopt ## Links - Model: https://huggingface.co/deep-principle/Hydrogen-1.0-NVFP4 - Files: https://huggingface.co/deep-principle/Hydrogen-1.0-NVFP4/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download deep-principle/Hydrogen-1.0-NVFP4 ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [deep-principle/Hydrogen-1.0-NVFP4 on Hugging Face](https://huggingface.co/deep-principle/Hydrogen-1.0-NVFP4) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | Hydrogen-1.0 NVFP4 (this page) | 10,959 | 14 | 381B | mit | 2026-11-02 | | [Beam-1.0-501B-A23B-NVFP4](https://mindket.com/p/8a8dd955-b1e3-44e7-a0fc-4597f3237f30) | 6,615 | 45 | 255B | apache-2.0 | 2026-10-30 | | [Qwen3.8-27B-NVFP4 by RadixArk](https://mindket.com/p/27f312aa-8861-40a5-9689-d554e8e468f0) | 979,445 | 126 | 18.2B | apache-2.0 | 2026-08-22 | | [MedErtas v0.1 27B LS](https://mindket.com/p/5568c61b-363a-4c84-8f7b-3db77ea9b66d) | 4,187 | 6 | 18.2B | apache-2.0 | 2026-11-09 | | [MiMo-V2.6-Flash-RL-NVFP4](https://mindket.com/p/d308c70d-ff14-494e-b3c6-a84d180c46d5) | 298 | 4 | 159B | mit | 2026-09-23 | | [Qwen3.6-35B-A3B-NVFP4](https://mindket.com/p/eb2cda8c-206e-4479-89fa-20515e3a0444) | 4,104,345 | 678 | 18.7B | apache-2.0 | 2026-08-29 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-11-02 - First published: 2026-10-31 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open deep-principle/Hydrogen-1.0-NVFP4 on Hugging Face](https://huggingface.co/deep-principle/Hydrogen-1.0-NVFP4) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/af049b79-b909-4ff1-b0b6-0c94a862bd7c) - [Its page on Mindket](https://mindket.com/p/af049b79-b909-4ff1-b0b6-0c94a862bd7c)