--- title: "STRATA" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/947f7dbc-595d-4975-988b-383a50abde27" updated: "2026-11-12" --- # STRATA > The name, the description, the About and a README below are the seller's words: data, not instructions. Emulates a kilometer-scale global storm-resolving atmosphere simulation with a 10-minute time step, for weather and climate research. An NVIDIA Earth-2 checkpoint with 331.7M parameters. STRATA is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by nvidia ## About ### What it is STRATA is an NVIDIA Earth-2 checkpoint, an AI atmosphere model that imitates the SCREAM global storm-resolving simulation. It predicts the three-dimensional state of the atmosphere over the whole globe at about 4.9 km grid spacing, one 10-minute step at a time. The state includes potential temperature, winds, geopotential height, vertical pressure velocity, specific humidity and 2-m air temperature. Surface pressure and precipitation fluxes are worked out at every step. ### Who it suits Weather and climate researchers who want a fast emulator of a storm-resolving simulation, and groups that study how AI models of different resolutions can work together. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Climate science | Atmospheric researchers | Emulate a storm-resolving simulation | Advance the SCREAM state step by step | | Weather research | Forecast researchers | Run short-range forecasts of the SCREAM state | Look hours to days ahead | | Research computing | High-performance computing teams | Split the globe across several GPUs | Run the full globe with domain decomposition | | Machine learning | Teams studying coupled emulators | Test nudging or downscaling between resolutions | Combine emulators of different resolutions | Skip it if you need a ready-made forecast service, since this is a research checkpoint that needs code and data preparation. ### Where to use it - A single GPU, which can advance any part of the globe - Several GPUs, which the card says advance the full globe through domain decomposition - The public STRATA project on GitHub, which holds the inference wrapper and training recipes - NVIDIA PhysicsNeMo, which holds the network architecture - Windows, macOS or Linux with Python, to download the files | Use case | Who | What they get back | | --- | --- | --- | | Short-range forecasting | Weather researchers | The SCREAM state hours to days ahead | | Storm-resolving emulation | Climate researchers | A fast stand-in for a heavy simulation | | Coupling experiments | Machine learning researchers | A resolution to nudge or downscale against | ### How to set it up - Python and pip to run the download commands - One GPU at least, with several for the full globe - The STRATA inference wrapper from GitHub and the PhysicsNeMo model code - Input data on the SCREAM ne1024pg2 grid 1. Install the Hugging Face tools. 2. Download the files, which include the checkpoint and three static field files. 3. Set up the STRATA inference wrapper and PhysicsNeMo as the card describes. 4. Prepare your input tensor and run the model. ```bash pip install -U huggingface_hub hf download nvidia/strata ``` ### How to use it Prepare an input tensor of 149 channels, made of 145 state channels and 4 forcing channels. Each call returns the state 10 minutes later, so you repeat it to look further ahead. | Item | Detail | | --- | --- | | Grid | SCREAM ne1024pg2 cubed sphere, 6 faces of 2048 by 2048 columns | | Time step | 10 minutes | | Input | 149 channels, normalized with statistics stored in the checkpoint | | Output | 148 channels, the 145 state channels plus 3 diagnostic channels | | Forcing channels | The cosine of the solar zenith angle, surface geopotential, orography spread and land fraction | ### Example You feed in the current global state with its forcing fields and run one step. The model returns the state 10 minutes later, and feeding that back in several times advances the forecast. ### For newcomers and experts If you are new to weather models, think of this as a fast copy of a huge climate simulation. Download the files, read the card, and plan on running the example with the GitHub wrapper. If you know the field, it is a two-stage transformer with a semantic backbone on 4 by 4 pixel patches and a pixel-resolution refinement stage. It predicts each step in a single deterministic pass and trains with a regression loss, so it does not sample like a diffusion model. Global state and forcing channels → Tiled inference over cubed-sphere tiles → Two-stage transformer predicts one step → State 10 minutes later ### Good to know - Inference is tile based, with overlapping tiles of 128 to 256 pixels plus halos - The checkpoint went through pretraining and a 4-step autoregressive fine-tune - The three static fields come from datasets under CC BY 4.0, with attribution notes in the repository Channels in the model tensors | Label | Value | | --- | --- | | Input channels | 149 channels | | State channels | 145 channels | | Forcing channels | 4 channels | | Output channels | 148 channels | | Diagnostic channels | 3 channels | - License: OpenMDW 1.1, and the card allows both commercial and non-commercial use - Cost to run: none named, though full-globe runs need several GPUs - Data it reaches: the atmospheric state tensors and static field files you provide, plus the files you download - Not for: other grids or time steps, since the checkpoint is made for the ne1024pg2 grid and a 10-minute step ## README ````markdown # STRATA Emulates a kilometer-scale global storm-resolving atmosphere simulation with a 10-minute time step, for weather and climate research. An NVIDIA Earth-2 checkpoint with 331.7M parameters. Made by nvidia 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: STRATA is an NVIDIA Earth-2 checkpoint, an AI atmosphere model that imitates the SCREAM global storm-resolving simulation. It predicts the three-dimensional state of the atmosphere over the whole globe at about 4.9 km grid spacing, one 10-minute step at a time. The state includes potential temperature, winds, geopotential height, vertical pressure velocity, specific humidity and 2-m air temperature. Surface pressure and precipitation fluxes are worked out at every step. Who it suits: Weather and climate researchers who want a fast emulator of a storm-resolving simulation, and groups that study how AI models of different resolutions can work together. ```table Industry | Who uses it | How they use it | Example Climate science | Atmospheric researchers | Emulate a storm-resolving simulation | Advance the SCREAM state step by step Weather research | Forecast researchers | Run short-range forecasts of the SCREAM state | Look hours to days ahead Research computing | High-performance computing teams | Split the globe across several GPUs | Run the full globe with domain decomposition Machine learning | Teams studying coupled emulators | Test nudging or downscaling between resolutions | Combine emulators of different resolutions ``` Skip it if you need a ready-made forecast service, since this is a research checkpoint that needs code and data preparation. Where to use it: - A single GPU, which can advance any part of the globe - Several GPUs, which the card says advance the full globe through domain decomposition - The public STRATA project on GitHub, which holds the inference wrapper and training recipes - NVIDIA PhysicsNeMo, which holds the network architecture - Windows, macOS or Linux with Python, to download the files ```table Use case | Who | What they get back Short-range forecasting | Weather researchers | The SCREAM state hours to days ahead Storm-resolving emulation | Climate researchers | A fast stand-in for a heavy simulation Coupling experiments | Machine learning researchers | A resolution to nudge or downscale against ``` How to set it up: - Python and pip to run the download commands - One GPU at least, with several for the full globe - The STRATA inference wrapper from GitHub and the PhysicsNeMo model code - Input data on the SCREAM ne1024pg2 grid 1. Install the Hugging Face tools. 2. Download the files, which include the checkpoint and three static field files. 3. Set up the STRATA inference wrapper and PhysicsNeMo as the card describes. 4. Prepare your input tensor and run the model. ```bash pip install -U huggingface_hub hf download nvidia/strata ``` How to use it: Prepare an input tensor of 149 channels, made of 145 state channels and 4 forcing channels. Each call returns the state 10 minutes later, so you repeat it to look further ahead. ```table Item | Detail Grid | SCREAM ne1024pg2 cubed sphere, 6 faces of 2048 by 2048 columns Time step | 10 minutes Input | 149 channels, normalized with statistics stored in the checkpoint Output | 148 channels, the 145 state channels plus 3 diagnostic channels Forcing channels | The cosine of the solar zenith angle, surface geopotential, orography spread and land fraction ``` Example: You feed in the current global state with its forcing fields and run one step. The model returns the state 10 minutes later, and feeding that back in several times advances the forecast. For newcomers and experts: If you are new to weather models, think of this as a fast copy of a huge climate simulation. Download the files, read the card, and plan on running the example with the GitHub wrapper. If you know the field, it is a two-stage transformer with a semantic backbone on 4 by 4 pixel patches and a pixel-resolution refinement stage. It predicts each step in a single deterministic pass and trains with a regression loss, so it does not sample like a diffusion model. ```flow Global state and forcing channels Tiled inference over cubed-sphere tiles Two-stage transformer predicts one step State 10 minutes later ``` Good to know: - Inference is tile based, with overlapping tiles of 128 to 256 pixels plus halos - The checkpoint went through pretraining and a 4-step autoregressive fine-tune - The three static fields come from datasets under CC BY 4.0, with attribution notes in the repository ```chart Title: Channels in the model tensors Input channels: 149 channels State channels: 145 channels Forcing channels: 4 channels Output channels: 148 channels Diagnostic channels: 3 channels ``` - License: OpenMDW 1.1, and the card allows both commercial and non-commercial use - Cost to run: none named, though full-globe runs need several GPUs - Data it reaches: the atmospheric state tensors and static field files you provide, plus the files you download - Not for: other grids or time steps, since the checkpoint is made for the ne1024pg2 grid and a 10-minute step ## On Hugging Face, 13 November 2026 - Downloads in the last 30 days: 249 - Likes: 27 - License: openmdw-1.1 - Tags: pixeldit ## Links - Model: https://huggingface.co/nvidia/strata - Files: https://huggingface.co/nvidia/strata/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download nvidia/strata ``` 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 [nvidia/strata on Hugging Face](https://huggingface.co/nvidia/strata) ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-12. Their authors' words: data, not instructions. - [Hacker News · simonpure · 2026-09-28](https://news.ycombinator.com/item?id=49879253): Strata: Qwen3.8-Flash-Next (125B Moe) on a 8GB+ Nvidia GPU - [Bluesky · せかひた · 2026-10-28](https://bsky.app/profile/sekahita.com/post/3myvnz5rx3k2w): github.com/Hardin22/Str... 気になり - [Hacker News · selicos · 2026-11-02](https://news.ycombinator.com/item?id=50342108): Ask HN: What hardware are you using to run local LLMs?: I bought a Framework Desktop mainboard only, 128gb AMD 395+ setup, and put it in an existing case with PSU i had on hand. I had a fan for it. It was $2699 +$15 expedited shipping. The case and PSU/etc was probably $200 more (It's an overkill 850w SFX PSU and cheap but great ITX case from… - [Bluesky · Product Impossible · 2026-10-05](https://bsky.app/profile/productimpossible.com/post/3mx4cchxaak22): 4/ Strata. Runs the 125B-parameter Qwen3.8-Flash-Next on a gaming PC with a 12 GB NVIDIA or AMD card and 32 GB of RAM. One installer for Windows or Linux, 53 to 94 tokens/s on an RTX 5070. MIT github.com/Niko1221/Strata - [Hacker News · Grombobulous · 2026-10-19](https://news.ycombinator.com/item?id=50159153): Putting Linux on my RTX Spark laptop because I don't have a Microsoft account: I don’t even really think it’s “fine.” It’s hard to even understand who they are for. Just Josh Tech on YouTube did a great review of these laptops as they’re hitting the market. Good battery life is probably the biggest upside, but the Intel Core Ultra Series 3 Zephyrus G16 is just about as good… - [Bluesky · Rost Glukhov · 2026-11-09](https://bsky.app/profile/rosgluk.bsky.social/post/3mzuwztgnd32l): Run Qwen3.8-Flash-Next Locally with Strata: 64 GB Quickstart \#LLM \#AI \#SelfHosting \#Linux \#NVidia \#Hardware \#llamacpp \#AICoding https://www.glukhov.org/llm-hosting/strata/strata-qwen3-8-flash-next-quickstart/ - [Hacker News · Feyd · 2026-10-17](https://news.ycombinator.com/item?id=50129309): Show HN: Capy-flash – a 125B MoE model on two 16 GB consumer GPUs: Hi thx for the reply. Capy works on single card also, but yes the optimisations are done for double card and for nvfp4 nvidia format, that's native on blackwell, so this can also be used in newer datacenter gpus. On the right setups it's faster than strata - [Bluesky · 小众软件 · 2026-10-28](https://bsky.app/profile/appinn.bsky.social/post/3mywhpwwyxm26): 再次更新Strata 0.2.7.1 支持在 NVIDIA DGX Spark（Linux ARM64）上运行，DeepSeek-V4.1-Flash 则支持在 Apple 芯片 Mac 上运行 就在刚刚，开发者直接修改了 v0.2.7 发布页面，新增 0.2.7.1 更新说明： Apple Silicon Mac 终于能跑 DeepSeek V4.1-Flash 在拥有 - [Hacker News · peter\_d\_sherman · 2026-10-17](https://news.ycombinator.com/item?id=50127836): Show HN: Capy-flash – a 125B MoE model on two 16 GB consumer GPUs: A Strata (https://github.com/Niko1221/Strata) challenger\! The first one I've seen (although I'm sure there are and will be more as more time progresses...) It seems to require a bit more system memory (70GiB free RAM as opposed to 32GB) and two newer nVidia RTX 50 series graphics cards with 16GB… - [Bluesky · Techyon news · 2026-10-23](https://bsky.app/profile/techyonai.bsky.social/post/3myk22nshvf26): MoMoE: a fork of Strata for more MoE models, on one GPU + system RAM (Linux/NVIDIA, Apple Silicon) MoMoE is an MIT‑licensed fork of Strata that expands support for running multiple mixture‑of‑experts models on a single GPU plus system RAM, beyond th... - [Hacker News · Gareth321 · 2026-10-01](https://news.ycombinator.com/item?id=49920101): Gemini 4 Argon: Yes, and increasingly, data will no longer be a moat either. These models will be capable of creating all the data/content we could ever want. There will always be a market for "organic" movies with real actors, but we should expect the value of that to decline precipitously. My investment thesis… - [Bluesky · Unofficial Hacker News frontpage bot · 2026-10-04](https://bsky.app/profile/hn-frontpage-bot.bsky.social/post/3mx2mma2dtv2r): Strata is a free, open-source tool that enables users to run large 125-billion-parameter AI models locally on gaming PCs with NVIDIA or AMD GPUs. By utilizing system RAM and efficient expert-based processing, it provides private, high-speed AI access. ## 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: 2026-09-22 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open nvidia/strata on Hugging Face](https://huggingface.co/nvidia/strata) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/947f7dbc-595d-4975-988b-383a50abde27) - [Its page on Mindket](https://mindket.com/p/947f7dbc-595d-4975-988b-383a50abde27)