DeepSeekDeepSeek V2 Lite Chat
DeepSeek develops the DeepSeek V 2 Lite Chat, a chat model capable of function calling, JSON mode, reasoning, streaming, and text generation. It is genuinely best at handling complex tasks with its mixture-of-experts architecture and multi-head latent attention, allowing for efficient inference.
Input
Output
Context
33K
Max Output
-
Parameters
15.7B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Resources & Links
Estimated GPU requirements for DeepSeek V2 Lite Chat
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 V2 Lite Chat
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
DeepSeek V2 Lite Chat advertises a context window of 32,768 tokens. The figure is the creator's published maximum; a given provider may serve less, and the gateway routes on what each provider actually serves.
Yes. DeepSeek V2 Lite Chat 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 V2 Lite Chat 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 V2 Lite Chat 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 V2 Lite Chat 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 15.9 GB at INT8 for the weights and a default context, from the catalog's 15.7B 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, provider APIs and benchmark publishers, each tagged with its source.
Last updated: Nov 4, 2026
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