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# Singapore's Sea-Lion v4 AI model fits on your laptop
- URL: https://www.techstories.co/singapores-sea-lion-v4-ai-model-fits-on-your-laptop/
- Published: 2025-08-28T17:04:00.000Z
- Updated: 2025-08-30T17:08:36.000Z
- Description: Quantised multimodal LLM runs on a laptop with 32GB RAM.
- Author: Paul Mah
- Tags: AI, Data Centres

The made-in-Singapore Sea-Lion v4 was released earlier this week, and it's the first multimodal model that will run on a laptop. Here's what we know about it.

### What's Sea-Lion?

AI Singapore's Sea-Lion project was designed to understand and represent Southeast Asia's linguistic and cultural diversity. According to AI Singapore's Leslie Teo, Sea-Lion v4 is ranked number one among open models under the 200B parameters mark on the SEA-HELM benchmark.

I've previously spoken with Leslie about Sea-Lion, who [shared his thoughts](https://www.techstories.co/made-in-singapore-the-story-behind-the-sea-lion-llm/) about ethical data sourcing, the strategy behind Sea-Lion, and why it was trained relatively cheaply. 

![](https://www.techstories.co/content/images/2025/08/1756461704605.jpg)

****Photo Credit**: Screenshot by Leslie Teo.

### Sea-Lion v4

I haven't had the chance to chat with Leslie again, but here's what we know about Sea-Lion v4\. It's the first multimodal release in the Sea-Lion family, based on Google's Gemma 3 (27B), supports 128K token context windows, and was trained on 1 trillion tokens from Southeast Asian corpus.

And yes, the quantised versions of Sea-Lion v4 will run on a laptop with 32GB RAM. That's not a typo. A model that understands Southeast Asian languages and can process images fits on hardware you probably already own.

Because Sea-Lion v4 is licensed under the commercially permissive Gemma license, you can use it in commercial deployments. No need to worry about licensing restrictions or enterprise agreements.

### The infrastructure reality check

As I wrote previously, the original Sea-Lion v1 was created with a lean team of just 20 Singaporeans. It was trained using 8x Nvidia A100 GPUs, and if I recall correctly, took less than a month of GPU time to train from scratch.

The team has since switched tactics. [Sea-Lion v2](https://www.techstories.co/ai-singapores-sea-lion-v2-ai-model-is-now-available/) was created with Meta's Llama 3 using pre-training and fine-tuning. This brought training time down to just two days with 64x Nvidia H100 GPUs. That's a massive reduction in computational requirements.

Yesterday, I wrote about the rack-level power requirements of enterprise data centres, [suggesting](https://www.techstories.co/the-rack-density-myth/) that 40-50kW should be adequate for a future-proofed deployment. From the figures above, it's clear that even a major LLM initiative like Sea-Lion, with a budget in the tens of millions (SGD), doesn't need many GPUs when using pre-training techniques.

So why would the average enterprise with just a handful or a score of GPUs require AI data centre-level rack density? They wouldn't.