> ## Content Index
> Fetch the complete content index at: https://www.techstories.co/llms.txt
> Use this file to discover other available public pages before exploring further.

# Why DeepSeek shocked AI experts and gained global fame
- URL: https://www.techstories.co/why-deepseek-shocked-ai-experts-and-gained-global-fame/
- Published: 2025-02-03T03:13:00.000Z
- Updated: 2025-02-08T16:22:53.000Z
- Description: Off the chart technical chops.
- Author: Paul Mah
- Tags: AI

You've read a lot about DeepSeek since last week. Hyperbole aside, here's why it is so good.

There's a lot going around right now about DeepSeek, much of it described using lofty language. What exactly does it do differently?

I explain in 4 points:

**Incredible optimisations, skill**

Limited to crippled China-only H800 GPUs, the DeepSeek team worked hard to optimise the limited resources they had.

- They used 8-bit floating point numbers (FP8) instead of traditional 32-bit (FP32) numbers. To make this work, they created a new system to break numbers into small tiles and blocks to switch between FP8 and FP32\. Result: 75% reduction in memory usage.
- To get more from their GPUs, the team implemented assembly-like programming (Nvidia PTX) instead of CUDA. Using PTX, they made optimisations to GPUs for efficiency and even reassigned some cores for server communication - likely for data compression to overcome limited bandwidth.

**Reinforcement learning**

DeepSeek was trained mainly using reinforcement learning with minimum reliance on supervised fine-tuning commonly used to train AI models.

- By relying mainly on automated feedback instead of human feedback, the cost and training time are significantly reduced.
- Or as described by Conor Grennan, "Most AI models need to see 1000's of solved examples first - DeepSeek let their model learn math through pure trial and error... like watching a kid figure out algebra by experimenting."

**They did it better**

The Mixture-of-Experts (MoE) architecture used by DeepSeek isn't new. MoE divides an AI model into sub-networks or experts for specific data or tasks.

OpenAI uses it. But where GPT-4 is believed to use just 16 expert models, DeepSeek did it at a far more granular level, thanks to various architectural innovations.

As a result, the DeepSeek model activates only 37 billion parameters out of its total 671 billion parameters for any given task. The result is similar performance at a fraction of the compute.

**The proof is in the price**

The biggest jaw-dropper is the cost of accessing it via API. For every million output tokens:

DeepSeek R1: $2.19  
OpenAI o1: $60.00

I'm not sure if the price is a result of DeepSeek's efficiency or improvements at the inference level, with some literature suggesting it uses Huawei's Ascend GPUs.

**Looking ahead**

DeepSeek released everything under the generous MIT license, including training secrets - far more than Meta's (not really open) open-source Llama.

And if DeepSeek shared so much voluntarily, what other tricks does the team have up its sleeve?