DEEPSEEK Open Source Week Day ONE - FlashMLA Tested & Explained

Timestamps:

00:00 - Intro
01:05 - FlashMLA First Look
03:52 - FlashMLA Install
05:00 - H100 Test
07:25 - Making Sense of This
11:45 - Similar Home Test
13:45 - Future Implications
14:39 - Closing Thoughts

DeepSeek’s Open Source Week begins with the release of FlashMLA, a technical repository designed to improve GPU efficiency in machine learning tasks. This high-performance optimization requires NVIDIA Hopper GPUs, such as the H100, to run the provided examples. While it has a high barrier to entry, the potential impact on AI workloads could be significant.

In this video, we take a first look at FlashMLA and test it on an H100 to evaluate its performance. We compare it to FlashAttention 2, assessing the speed and efficiency improvements it brings to large-scale GPU workloads. To understand how it might impact a broader audience, we also attempt a home test on 3000 and 4000 series NVIDIA GPUs to see how it holds up outside of a high-end enterprise setup.

Beyond testing, we explore what FlashMLA means for the future of AI acceleration, including its potential influence on consumer GPUs. We also take a brief look at FlashAttention 3, which could further shape the way GPUs handle large-scale inference and training tasks.

This video serves as an introduction to FlashMLA, its practical applications, and its potential long-term impact on GPU efficiency. Whether you are a researcher, developer, or AI enthusiast, this breakdown provides insight into how these optimizations could shape the future of machine learning hardware.