Technology
For our initial launch, Ceramic provides a training stack optimized for Transformer++ models. Our stack rewrites end-to-end, redefines the boundaries of math, networking and compute balanced for different machine architectures.
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Ceramic's Tech Stack Reimagines Pre-training & Post-training
Pre-Training
Ceramic's training stack is cluster-aware, ensuring efficient coordination across nodes for seamless transformer++ training. This enables balanced and synergistic operations across the entire cluster.
Post-training
Ceramic offers robust tools and partnerships to streamline fine-tuning, assisting clients with data collection and metric setting to align with their specific needs.
Why long-context?
Why long-context?
Long-context is increasingly vital for advanced reasoning tasks and applications that demand a deep understanding of complex information (e.g. coding, research, finance, legal, etc).
It prevents context fragmentation, enhances memory retention, and enables more complex problem solving. By integrating reasoning models with long-context training, AI systems can not only “remember” better but also “think” more critically, driving innovations in industries that rely on deep contextual insights and strategic decision-making.
Our proposition
Faster, Scalable & Long-context
Faster at Small models, but enables large model long-context training.
Efficiency (2.5x) on 8B bf16 models - head to head faster