Dspark_qwen 3_8b_block 7
Dspark_qwen 3_8b_block 7 is a 7-billion parameter language model developed by DeepSeek. It features a notably large context window of 40,960 tokens, enabling it to process extensive documents or long conversational threads.
Input
Output
Context
41K
Max Output
4K
Parameters
2.4B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Resources & Links
Estimated GPU requirements for Dspark_qwen 3_8b_block 7
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 Dspark_qwen 3_8b_block 7
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Dspark_qwen 3_8b_block 7 advertises a context window of 40,960 tokens, with a maximum output of 4,000 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. Dspark_qwen 3_8b_block 7 is an open-weight model released under the DeepSeek Model License license, so the weights can be downloaded and self-hosted within that license's terms.
Dspark_qwen 3_8b_block 7 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 Dspark_qwen 3_8b_block 7 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; Dspark_qwen 3_8b_block 7 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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