Source URL: https://www.tomshardware.com/tech-industry/artificial-intelligence/deepseeks-ai-breakthrough-bypasses-industry-standard-cuda-uses-assembly-like-ptx-programming-instead
Source: Hacker News
Title: DeepSeek’s AI breakthrough bypasses industry-standard CUDA, uses PTX
Feedly Summary: Comments
AI Summary and Description: Yes
Summary: DeepSeek’s recent achievement in training a massive language model using 671 billion parameters has garnered significant attention due to its innovative optimizations and the use of Nvidia’s PTX programming. This breakthrough enhances efficiency in AI model training, signaling potential shifts in hardware requirements and market dynamics.
Detailed Description: The text discusses DeepSeek’s groundbreaking advancements in AI by training an exceptionally large language model, highlighting several technical aspects:
– **Massive Language Model**: DeepSeek trained a Mixture-of-Experts (MoE) model with 671 billion parameters, utilizing a powerful computing infrastructure of 2,048 Nvidia H800 GPUs over a two-month period.
– **Efficiency Claims**: The training process reportedly achieved 10 times the efficiency of established leaders in the AI industry, such as Meta, indicating a significant advancement in resource utilization and performance.
– **PTX vs. CUDA**:
– DeepSeek utilized Nvidia’s PTX (Parallel Thread Execution) programming architecture instead of traditional CUDA, which is known for higher-level GPU programming.
– The PTX architecture allows for fine-grained optimizations, enabling better control over GPU resources at a more granular level, such as register allocation and thread management.
– **Architectural Adjustments**:
– Specifically, 20 out of 132 streaming multiprocessors on the Nvidia H800 GPUs were reallocated for server-to-server communication, likely aimed at optimizing data transfer needs.
– Advanced pipeline algorithms and thread/warp-level adjustments were also deployed to enhance performance, demonstrating DeepSeek’s engineering expertise and commitment to detailed optimization.
– **Market Implications**:
– DeepSeek’s success may disrupt the AI hardware market, with speculation that the requirements for high-performance hardware will decrease, potentially affecting Nvidia’s sales.
– Industry experts, including former Intel CEO Pat Gelsinger, believe such advancements can democratize AI by making it accessible across a wider array of devices.
– **Challenges**:
– The high level of optimization comes with difficulties in maintenance, indicating that while DeepSeek’s approach is highly effective, it requires a skilled workforce and possibly significant financial investments.
In summary, DeepSeek’s advancements not only underscore a leap in AI training methodologies but might also reshape the competitive landscape of AI hardware demand and accessibility, emphasizing the importance of optimized computing solutions in the AI sector.