NishMath

DeepSeek has made significant advancements in AI model training and efficiency with its Fire-Flyer AI-HPC infrastructure. This homegrown infrastructure enables the training of trillion-parameter models with unprecedented cost efficiency. What makes this software-hardware co-design framework even more remarkable is that DeepSeek has accomplished this infrastructure feat with a team of fewer than 300 employees, showcasing their deep technical expertise in building a system optimized for high-speed data access and efficient computation.

The Fire-Flyer AI-HPC infrastructure is specifically designed for training and serving deep learning models and Large Language Models (LLMs) at scale. It leverages thousands of GPUs to accelerate computationally intensive tasks, a custom-built distributed file system for high-speed data access, and efficient inter-GPU communication. DeepSeek has replicated the "thinking" token behavior of OpenAI's o1 model and published the full technical details of their approach in their "DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning" paper.

ImgSrc: i0.wp.com

References :

• Gradient Flow: DeepSeek Fire-Flyer: What You Need to Know
• lambdalabs.com: How to serve DeepSeek-R1 & v3 on NVIDIA GH200 Grace Hopper Superchip (400 tok/sec throughput, 10 tok/sec/query)
• LearnAI: Customize DeepSeek-R1 distilled models using Amazon SageMaker HyperPod recipes – Part 1

Classification:

• HashTags: #DeepSeek #OpenSourceAI #InferenceEfficiency
• Company: DeepSeek
• Target: AI researchers
• Product: Fire-Flyer
• Feature: AI-HPC infrastructure
• Type: AI
• Severity: Informative