Deepseek Vl2
DeepSeek-VL2 is a chat model developed by DeepSeek, capable of handling text and image inputs, and exceling at tasks such as visual question answering, optical character recognition, and document understanding. It utilizes a Mixture-of-Experts (MoE) architecture, with variants ranging from 1.0B to 4.5B activated parameters, achieving competitive performance with fewer parameters compared to similar models.
Input
Output
Context
4K
Max Output
1K
Parameters
27.5B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Resources & Links
Estimated GPU requirements for Deepseek Vl2
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 Vl2
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Deepseek Vl2 advertises a context window of 4,096 tokens, with a maximum output of 512 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 Vl2 is an open-weight model released under the Other license, so the weights can be downloaded and self-hosted within that license's terms.
Deepseek Vl2 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 Deepseek Vl2 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 Vl2 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 27.1 GB at INT8 for the weights and a default context, from the catalog's 27.5B 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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