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LatestDeepSeekV4-Flash
DeepSeekText / LLM

DeepSeek Releases V4-Flash, a Fast MIT-Licensed MoE Model

The new Mixture of Experts model from the Beijing-based AI lab is optimized for fast, efficient conversational AI and carries a fully permissive license.

Apr 22, 2026
Major releaseMIT
DeepSeek V4.1 Flash

AI research lab DeepSeek has released DeepSeek-V4-Flash, a new open-source model designed for speed and efficiency in conversational tasks. This release continues the company's track record of publishing capable models for the open-source community.

The model's name points to its primary strengths. As a Mixture-of-Experts (MoE) architecture, it activates only a subset of its parameters for any given input, leading to faster inference times. The 'Flash' designation is further supported by its use of FP8 weights, a form of quantization that reduces the model's memory footprint and computational cost.

Perhaps most notable is the choice of license. DeepSeek-V4-Flash is released under the MIT License, one of the most permissive open-source licenses available. This allows for unrestricted use, modification, and distribution, including for commercial purposes, removing a significant barrier to adoption for many startups and enterprises.

Why it matters: The combination of an efficient MoE architecture and a truly open, commercially friendly license makes DeepSeek-V4-Flash a compelling choice for developers building real-time, interactive AI applications. For teams prioritizing low latency without compromising on conversational quality, this model presents a significant new option in the open-source landscape. The full model weights are available on Hugging Face.

Sources

  • deepseek-ai/DeepSeek-V4-Flash

    Hugging Face

    Visit
  • unsloth/DeepSeek-V4-Flash-GGUF

    Hugging Face

    Visit
OlderSenseTime Releases 8B Any-to-Any Multimodal ModelSenseTime · Any-to-Any · 6 months agoNewerDeepSeek Releases V4-Pro, an Open MoE ContenderDeepSeek · Text / LLM · 6 months ago

Get the model

Hugging FaceHugging Face

Specs

Active params13B active
Context window1.0M tokens
Size292.5 GB
PrecisionINT8
ArchitectureDeepseekV4ForCausalLM
LicenseMIT
Downloads1.1M
Likes2.4K

Can you run it?

Needs a multi-GPU server — about 176.3 GB at 4-bit.

  • FP8 (as published)

    4×H100 80 GB · 512 GB Mac Studio

    294.2 GB
  • 4-bit

    4×H100 80 GB · 512 GB Mac Studio

    176.3 GB
Your machine
GGUF builds
  • Mixture-of-experts: all 291B parameters must be resident even though only 13B are active per token — active count affects speed, not memory.
  • KV cache sized for 8,192 tokens of context; longer prompts need proportionally more.

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


Modalities

Text / LLMReasoning
3 versions — view changelog

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