Deepseek R1 Zero
DeepSeek R 1 Zero is a chat model developed by DeepSeek, exceling at reasoning and chain-of-thought tasks through its unique training via large-scale reinforcement learning without supervised fine-tuning. This approach enables the model to generate long chains of thought and demonstrate self-verification and reflection capabilities.
Input
Output
Context
131K
Max Output
33K
Parameters
684.5B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Resources & Links
Estimated GPU requirements for Deepseek R1 Zero
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 Deepseek R1 Zero
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Deepseek R1 Zero advertises a context window of 131,072 tokens, with a maximum output of 32,768 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. Deepseek R1 Zero is an open-weight model released under the MIT license, so the weights can be downloaded and self-hosted within that license's terms.
Deepseek R1 Zero accepts Text 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 Deepseek R1 Zero 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; Deepseek R1 Zero 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 659.2 GB at INT8 for the weights and a default context, from the catalog's 684.5B 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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