DeepSeek-V4-Flash

Updated
24.06.2026
Thinking
Reasoning
Code
Multilingual
Run

huggingface-cli download deepseek-ai/DeepSeek-V4-Flash
from transformers import AutoModel
model = AutoModel.from_pretrained("deepseek-ai/DeepSeek-V4-Flash")
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NameSize / UsageContextInput
DeepSeek-V4-Pro
1MText
At a glance
  • License: Mit
  • Context length: 1M tokens
  • Languages: Multilingual
  • Minimum hardware: ~89 GB VRAM
  • Strengths: reasoning, coding and on-device inference
Overview
DeepSeek-V4-Flash is an open-weight large language model from deepseek-ai, the lab behind the DeepSeek-V3 and R1 series. It uses a Mixture-of-Experts (MoE) design from the deepseek_v4 architecture, so only a fraction of its parameters fire on any given token. That keeps inference fast and memory pressure lower than a dense model of the same headline size would suggest. The release ships in FP8 and carries a 158.1B parameter count with a context window of 1,048,576 tokens.
The local-AI angle is the point. Through Atomic Chat you load DeepSeek-V4-Flash and run it fully on your own machine, so prompts and outputs never leave your hardware. There is no API key, no per-token bill, and no network round-trip once the weights are downloaded. It works offline and stays private to your device.
What it is good at
The model lists thinking, reasoning, code, and multilingual capabilities, which line up with these jobs.
  • Step-by-step reasoning — the thinking and reasoning capabilities let it work through math, logic, and multi-step problems before committing to an answer, rather than guessing in one pass.
  • Code generation and review — the code capability covers writing functions, explaining unfamiliar snippets, and tracing bugs across a long file thanks to the 1,048,576-token context.
  • Multilingual work — the multilingual capability handles drafting, translation, and Q&A across languages without sending your text to a hosted service.
Running it locally
At 158.1B parameters the full FP8 weights are large, but the MoE routing means active compute per token stays modest. The 1,048,576-token context lets you feed entire codebases or long documents in one go, provided your RAM and VRAM can hold the working set. Grab the weights from the deepseek-ai repository on Hugging Face:
huggingface-cli download deepseek-ai/DeepSeek-V4-Flash
From there you can serve it with Transformers or vLLM, or skip the setup entirely and load DeepSeek-V4-Flash with one click inside Atomic Chat, which manages the download and runtime for you.
License
DeepSeek-V4-Flash is released under the MIT license. That permits commercial use, modification, redistribution, and private deployment, as long as the copyright and license notice stay with the code. It is one of the most permissive licenses available, so you can build on the model without negotiating separate terms.
Desktop



macOS
(M1 or better)
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Windows
(x64)
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Linux
(x86_64)
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Frequently asked questions
DeepSeek-V4-Flash is an open-weight Mixture-of-Experts language model from deepseek-ai, part of the deepseek_v4 family. It has a 158.1B parameter count, ships in FP8, and supports a 1,048,576-token context window. Its listed capabilities are thinking, reasoning, code, and multilingual, and it is built so only some experts activate per token to keep inference efficient.
The full FP8 weights are 158.1B parameters, so you need a machine with substantial memory, typically a high-VRAM GPU setup or a workstation with large unified memory. The MoE design keeps active compute per token lower than the headline size implies, which helps. Atomic Chat handles the download and runtime, so you mainly need enough RAM and VRAM to hold the model and your context.
Yes. The weights are released under the MIT license, and running the model locally through Atomic Chat costs nothing per token. There is no API key or subscription. You only pay for the hardware and electricity to run it on your own machine.
Yes. Once you download the weights with Atomic Chat, the model runs fully on your device with no internet connection required. Prompts and responses stay local, which keeps your data private. You only need a connection for the initial download of the model files.
You can download the weights from the deepseek-ai repository on Hugging Face with huggingface-cli download deepseek-ai/DeepSeek-V4-Flash, then serve them using Transformers or vLLM. The simpler path is to open Atomic Chat and load DeepSeek-V4-Flash with one click, which sets up the download and runtime for you. After that you chat with it directly, offline and on your own hardware.