Why We Invested in Ceramic.ai: Transforming AI Model Training Efficiency
As large language models (LLMs) continue to push the boundaries of artificial intelligence, traditional methods of scaling—mainly by adding more GPUs—are reaching their limits. These approaches are resulting in diminishing returns and rapidly increasing costs. Ceramic.ai is addressing these challenges by revolutionizing AI infrastructure. Their technology tackles the bottlenecks in parallel processing that have long hindered training efficiency and slowed innovation in both research and enterprise applications.
Ceramic.ai’s breakthrough parallelism technology significantly speeds up AI training by splitting a single pre-training context across multiple GPUs. This method achieves up to 2.5 times higher efficiency compared to open-source stacks. Unlike conventional techniques, Ceramic.ai excels at training large models on long-context data, maintaining high efficiency even for models with over 70 billion parameters. This advantage translates into impressive real-world performance. For example, their reasoning models scored 92% Pass@1 on the GSM8K benchmark, outperforming Meta’s Llama 70B 3.3 base model, which scored 79%, and DeepSeek R1, which scored 84%.
How Ceramic.ai’s Technology Advances AI Scalability and Efficiency
Ceramic.ai’s technology integrates smoothly with existing parallelism methods while introducing a novel approach to reorder training data by topic. This reordering enhances attention efficiency and eliminates token masking issues, marking a fundamental leap forward in AI scalability and optimization. Their innovation not only accelerates training but also reduces the compute costs associated with developing large models.
We are excited to participate in Ceramic.ai’s $12 million seed funding round alongside NEA, IBM, and Earthshot Ventures. The timing of this investment is ideal. As enterprises worldwide race to create and deploy custom AI models, efficient training has become a critical competitive advantage. Ceramic.ai’s technology lowers the financial barriers to training advanced AI, making it accessible to a wider range of organizations.
Beyond enterprise applications, Ceramic.ai’s work has promising implications for mobile and edge computing. Their hybrid cloud and on-device inference technology aims to transform AI deployment on consumer devices. This innovation promises reduced latency and improved accuracy, which could lead to vastly better AI experiences on mobile and IoT devices.
Why We Invested in Ceramic.ai: Leadership and Market Potential
Ceramic.ai is led by Dr. Anna Patterson, a seasoned expert with a remarkable history of scaling transformative technologies. During her 17 years at Google, Dr. Patterson architected TeraGoogle, the company’s large-scale search serving system, showcasing her deep expertise in distributed computing. She also played a key role in scaling Android from 40 million to over 1 billion phones, providing valuable insights into deploying infrastructure at massive scale. Additionally, as Founder and Managing Partner of Gradient Ventures, she has demonstrated a keen ability to identify and nurture groundbreaking AI technologies.
Our investment in Ceramic.ai goes beyond supporting promising technology. It represents backing a fundamental shift in how AI systems are built and deployed within enterprises. Ceramic.ai’s parallelism technology addresses a critical bottleneck in AI development, enabling the creation of more efficient and powerful models. For organizations developing custom AI solutions, this means faster iteration cycles and reduced costs. For those deploying AI at the edge, it opens new possibilities for mobile and IoT applications.
The combination of innovative technology, perfect market timing, and an exceptional founding team makes Ceramic.ai a compelling investment. As AI continues to reshape industries, Ceramic.ai’s infrastructure advancements will be essential in enabling the next generation of AI applications and services for enterprises worldwide.
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