Several GPUs are needed by flagship models, the ones comparable to the best cloud services. It is an expensive setup, and it makes sense only after a smaller model has been tested on your tasks and fell short.
Flagship open models from Mira Murati's lab: they take text, images and audio. Large MoE models that need several GPUs.
Large MoE models with very long context (up to 1M tokens for Text-01 and M3). M3 is multimodal and understands images. Licenses differ greatly from version to version.
xAI publishes the weights of previous Grok generations. The models are very large and need a GPU cluster, so in practice they are rarely run.
Open weights of MiniMax's Hailuo video model. A large 33B model that makes video from text and images, but needs several server GPUs.
A large 30B video model producing clips of up to 204 frames. Needs server hardware, but is open under MIT.
A large model for financial documents with a context window of about 131k tokens: it holds long reports whole. Not investment advice: decisions are made by a specialist.
Large NVIDIA scorers for selecting and fine-tuning answers. The multilingual GenRM version lists Russian among its languages. The scorer itself makes mistakes and does not replace manual review on important tasks.
The usual mistake is to start with the flagship. In practice it goes the other way: a small model on one GPU, quality measured on your own examples, and only then a move up. A cluster adds more than hardware cost: networking, cooling, updates and someone on call.
An open model can run on your own server: data stays in-house, there is no per-request fee, and the model can be fine-tuned on your documents.
