DeepSeek-V4-Flash 284B can now run on just 5.3 GB of memory
New extreme quantisation makes it possible to run the DeepSeek-V4-Flash AI model, featuring 284 billion parameters, on only 5.3 GB of memory. This enables local execution on standard consumer hardware.
What happened?
A new quantised version of the DeepSeek-V4-Flash AI model with 284 billion parameters has been published by the community on the Reddit forum r/LocalLLaMA. Through extreme quantisation, the model's memory footprint has been reduced to 5.3 gigabytes of RAM/VRAM. This makes it possible to run a large language model on standard consumer PCs without the need for expensive, specialised AI graphics cards.
Key facts
| Modellnamn | DeepSeek-V4-Flash |
|---|---|
| Antal parametrar | 284 miljarder (284B) |
| Minneskrav | 5,3 GB RAM/VRAM |
Why it matters
Traditionally, models in the 300-billion parameter class require multiple high-performance enterprise graphics cards with hundreds of gigabytes of VRAM to function. By reducing the memory requirement to under 6 GB, advanced AI technology becomes available to a significantly wider group of users, accelerating experimentation with local AI systems.
Who is affected?
The news primarily concerns AI developers, hobbyists, and researchers who wish to run large language models locally without cloud services. It is also of interest to companies seeking cost-effective solutions for local inference on existing hardware.
Impact on the EU
The model is distributed as open source and is not subject to geographical restrictions, making it available to developers and researchers within the EU. Use within the EU, however, requires that the processing of personal data complies with the GDPR and the EU AI Act.
What else you should know
Discussions on Reddit indicate strong interest in the heavy quantisation of large language models, as it lowers the barrier for who can run advanced AI locally. At the same time, experienced developers point out that such extreme compression can impact the model's capacity for logical reasoning and complex problem-solving.
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