As AI evolves, developers and researchers are constantly seeking platforms that provide both performance and accessibility for their experiments. The latest breakthrough in this field is the DeepSeek-R1 model, a large language model (LLM) pushing the boundaries of problem-solving, creativity, and logical reasoning.
Matthew Berman, a tech reviewer from Forward Future, recently uploaded a fascinating YouTube video exploring what it’s like to run DeepSeek-R1 using AMD AI infrastructure powered by the AMD Instinct™ MI300X accelerator. With Vultr powering the underlying cloud setup, providing Vultr Cloud GPU AMD Instinct™ MI300X accelerators, the results highlight an exciting new era of AI experimentation.
Putting DeepSeek-R1 to the test
In his video, Matthew takes DeepSeek-R1 through a comprehensive series of tasks designed to assess its problem-solving abilities and performance on AMD Instinct™ MI300X accelerator hardware. Some key highlights include:
- Game development challenges: DeepSeek-R1 is tasked with building games like Snake and Tetris in Python. Impressively, the model writes clean, functional code for both tasks, culminating in 179 lines of code for the Tetris challenge.
- Logic problem-solving: From counting words in a sentence to tackling abstract logic puzzles, DeepSeek-R1 demonstrates a humanlike thought process and consistently delivers accurate results.
- Censorship awareness: The model reflects a nuanced understanding of censorship, thoughtfully responding to sensitive questions during the testing process.
Powered by AMD on Vultr
What makes this testing even more remarkable is the hardware supporting DeepSeek-R1. Matthew emphasizes the "insane performance" provided by a combination of AMD EPYC CPUs and AMD Instinct™ GPUs running on Vultr’s cloud infrastructure. Vultr’s platform not only delivers the necessary computational power but also simplifies access to AMD’s AI hardware, enabling seamless testing for developers worldwide.
As Matthew points out, Vultr’s hardware configurations are optimized for workloads like LLMs, providing scalable, cost-effective options for AI enthusiasts and professionals alike.