According to @godofprompt, OpenAI's O1 model demonstrates that enhancing model intelligence can be effectively achieved by increasing inference-time computation rather than simply expanding model size (source: @godofprompt, https://x.com/godofprompt/status/2011722597797675455). Major industry players including DeepSeek, Google, and Anthropic are now shifting their strategies to focus on test-time compute, signaling a paradigm shift away from the traditional 'training wars' and towards an 'inference war.' This trend opens up significant business opportunities for AI companies to develop optimized inference frameworks and infrastructure, catering to the growing demand for smarter, more efficient AI applications. The move towards test-time compute is expected to drive innovation in AI deployment, reduce costs, and enable more scalable commercial solutions.

According to Andrej Karpathy, the Llama 1B AI model can now perform batch-one inference using a single CUDA kernel, eliminating the synchronization boundaries that previously arose from sequential multi-kernel execution (source: @karpathy, Twitter, May 27, 2025). This approach allows optimal orchestration of compute and memory resources, significantly improving AI inference efficiency and reducing latency. For AI businesses and developers, this technical advancement means faster deployment of large language models on GPU hardware, lowering operational costs and enabling real-time AI applications. Industry leaders can leverage this progress to optimize their AI pipelines, drive competitive performance, and unlock new use cases in edge and cloud AI deployments.