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LatestDeepSeekV4
DeepSeekText / LLM

DeepSeek Releases V4-Pro, an MIT-Licensed MoE Model

The new flagship arrives as a mixture-of-experts system with FP8 weights and open reasoning capabilities under a permissive license.

Jun 27, 2026
Major releaseMIT
DeepSeek-V4-Pro

DeepSeek has released DeepSeek-V4-Pro, the latest entry in its widely watched line of open models, publishing the weights on Hugging Face. The model is a mixture-of-experts (MoE) system shipped with FP8 weights and made available under the permissive MIT license.

The release is positioned as a text and reasoning model, continuing DeepSeek's push into systems that can handle both general language tasks and multi-step problem solving. As with the company's earlier work, distributing the model in FP8 format signals an emphasis on efficient inference, letting the large expert-based architecture run with a smaller memory footprint than full-precision equivalents.

Why it matters

DeepSeek has become one of the most closely tracked labs in open-weight AI, and each major version tends to reset expectations for what freely available models can do. A few points stand out here:

  • MIT licensing places few restrictions on commercial and research use, a key draw for teams building on open models.
  • Mixture-of-experts design activates only part of the network per token, a route to scaling capacity without proportional compute cost.
  • FP8 weights lower the barrier to deployment on constrained hardware.

Detailed specifications such as parameter counts, context length, and benchmark results were not included in the release record, so builders will want to consult the official model card as DeepSeek fills in documentation. For now, the arrival of a new MIT-licensed flagship gives the open-source community another capable foundation to test, fine-tune, and deploy.

Sources

  • deepseek-ai/DeepSeek-V4-Pro-DSpark

    Hugging Face

    Visit
OlderDeepSeek Releases V4-Flash for Low-Latency InferenceDeepSeek · Text / LLM · 4 months agoNewerSenseTime's SenseNova-Vision-7B-MoT Goes Any-to-AnySenseTime · Any-to-Any · 4 months ago

Get the model

Hugging Face

Specs

Context window1.0M tokens
Size842 GB
PrecisionINT8
ArchitectureDeepseekV4ForCausalLM
LicenseMIT
Downloads367.5K
Likes5.7K

Can you run it?

Datacenter only — about 844 GB even as published.

  • FP8 (as published)

    Multi-node cluster

    844 GB
Your machine
  • Published weights are already quantized (FP8); smaller builds are not assumed.
  • KV cache sized for 8,192 tokens of context; longer prompts need proportionally more.
  • No parameter count available, so quantized sizes cannot be derived.

Based on Hugging Face weights metadata. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Text / LLMReasoning
2 versions — view changelog

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