Cosmos Reason 2 2B
NVIDIA's Cosmos Reason 2 2B is a 2B-parameter reasoning vision language model that enables robots and vision AI agents to reason like humans, using prior knowledge, physics understanding, and common sense. It is genuinely best at understanding space, time, and fundamental physics, making it suitable for applications such as robot planning and reasoning, video analytics, and data curation.
Input
Output
Context
256K
Max Output
4K
Parameters
2.4B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Resources & Links
Estimated GPU requirements for Cosmos Reason 2 2B
Estimates based on INT8 quantization at up to 32K context. A count above one assumes tensor parallelism across the cards. Actual requirements vary by framework and configuration.
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Frequently asked questions about Cosmos Reason 2 2B
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Cosmos Reason 2 2B advertises a context window of 256,000 tokens, with a maximum output of 4,096 tokens in a single response. The figure is the creator's published maximum; a given host may serve less, and the gateway routes on what each host actually serves.
Yes. Cosmos Reason 2 2B is an open-weight model released under the Other license, so the weights can be downloaded and self-hosted within that license's terms.
Cosmos Reason 2 2B accepts Text and Image and produces Text. The capabilities card on this page lists which API features each deployment honours, such as function calling and structured output, with the source each was checked against.
No published benchmark result for Cosmos Reason 2 2B is in the catalog yet, so the model is shown as unmeasured. It is not ranked or estimated; the router treats it as unknown for every task until a suite measures it.
Not on the managed pool today; Cosmos Reason 2 2B is listed for reference and comparison. Connect your own provider key or endpoint that serves it and the gateway runs it on your account, with routing decisions recorded the same way.
About 2.8 GB at INT8 for the weights and a default context, from the catalog's 2.4B parameter count; FP16 needs roughly twice that, and long contexts or many concurrent requests add KV cache on top. The GPU section on this page lists cards that hold it, and the capacity planner sizes it for your context length and traffic.
Fields collected from public registries, host APIs and benchmark publishers, each tagged with its source.
Last updated: Aug 28, 2026
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