The new partnership with NVIDIA evolves the long-standing collaboration between the two companies. OpenAI has pledged to consume 2 gigawatts of training capacity on NVIDIA's Vera Rubin systems and an additional 3 gigawatts of computing resources, likely in the form of GPUs, to run specific AI inference tasks. In other words, NVIDIA is spending a lot of money on OpenAI and then OpenAI will turn around and spend a lot of money with NVIDIA. The ouroboros must feed.
Can these agent-benchmaxxed implementations actually beat the existing machine learning algorithm libraries, despite those libraries already being written in a low-level language such as C/C++/Fortran? Here are the results on my personal MacBook Pro comparing the CPU benchmarks of the Rust implementations of various computationally intensive ML algorithms to their respective popular implementations, where the agentic Rust results are within similarity tolerance with the battle-tested implementations and Python packages are compared against the Python bindings of the agent-coded Rust packages:
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Option 2: For very localized changes, it might even re-evaluate all shortcuts within that one affected cluster.