SAVRN Model Hub
Open-Weight Models
An open-weight model is an AI model whose trained weights are published for anyone to download. The weights are what the model learned in training. With a copy of them you can run the model on hardware you control and train it further on your own data.
Open weights are not the same as open source. Many publishers release the weights without the training data or code, and the license sets what you may do with the model. This library puts each model's full card, architecture, files, license and published evaluations on one page.
Updated 2026-09-18 · How the library is built
2,760 models, sorted by most downloaded.
A 2,067,130,880-parameter decoder-only transformer whose entire forward pass, backward pass and optimizer step run on hand-written x86-64 NASM kernels (AVX2+FMA), driven by a pure C runtime with no Python in the training loop. complex/quantum attention, no low-rank factorization. It exists as a clean CPU control alongside the main BulmaX multimodal run, on the same tokenizer (bulmasp.model) for data comparability. Raw binary BULMAXCP v1 -- not safetensors, not pickle. Fixed 64-byte header, 64-byte directory entries, 64-byte-aligned float32 blobs, so a C or assembly loader can mmap it and walk the directory with no JSON parser and no Python. Files hold weights plus both AdamW moments and the…
this is just for transfering checkpoints between instances, why are you here PLEASE STOP LIKING THIS ONE
CARI4D CoCoNet Native Momentum Human Rig (MHR) refines one human's native MHR parameters and one rigid object's pose over video and predicts one contact logit per hand. It supports 4D human-object reconstruction, motion analysis, visualization, data curation, and robotics perception without direct autonomous actuation. CARI4D CoCoNet Native MHR was developed by NVIDIA Research. This model is ready for commercial or non-commercial use. GOVERNING DOWNLOAD TERMS: Use of the model is governed by the NVIDIA Open Model Agreement. Global. For developers and researchers building commercial or noncommercial computer-vision systems for 3D/4D human-object pose estimation, motion analysis…
Model Collections
Hand-picked starting points, each with the reason it exists.
Collection · 4 entries
Embedding models for retrieval
Sentence and document embedding models used to build retrieval systems. Dimension and sequence length matter more than size here, and both come from the publisher.
Collection · 4 entries
Models that fit on one accelerator
Models whose publisher-reported parameter count puts them within reach of a single accelerator at common precisions. Memory needed depends on precision and serving configuration, so treat the parameter count as the starting point, not the answer.
Collection · 6 entries
Open-weight text models worth knowing
Widely used open-weight language models, chosen because each one is a distinct family rather than a variant of the one above it. Selection, not a ranking.
Collection · 3 entries
Speech and audio models
Recognition and synthesis models, grouped so the two directions are easy to compare.
Open-Weight Models Explained
What is an open-weight model?
An AI model whose trained weights are published for anyone to download, so it can be run, tested and fine-tuned on hardware the user controls.
Is an open-weight model the same as open source?
Not always. Open weights means the trained model can be downloaded. Open source usually also means the training code and data are available and the license allows broad reuse. Many open-weight models release the weights only.
Can I use an open-weight model commercially?
It depends on the license. Apache 2.0 and MIT allow commercial use. Other licenses limit it, for example to non-commercial use or below a set number of users. Every model page here shows its license.
How much memory does an open-weight model need?
About two bytes per parameter at 16-bit precision, so a 7-billion-parameter model needs roughly 14 GB for its weights, plus memory for the context it processes. Each model page lists its parameter count and the size of its files.
Related SAVRN Research
The hub sits beside SAVRN's market data and infrastructure research: what models cost to run, and what it takes to run them.
SAVRN Index
What open models cost to run
The same open-weight model priced by every host that serves it, per million tokens.
Research Hub
Data center trackers and maps
Moratoriums, permits, power, water and capital behind the facilities that run these models.
Method
How the Model Hub is built
Sources, evidence labels, refresh behaviour, and the limits of every comparison here.