It can perform cognitive functions that previously only humans could do, such as perceiving interactions across different modalities and environments - with the ability to quickly learn and then solve complex problems.
Tensordyne is an AI system solution company that builds very high-performance, low-power generative AI inference systems.
Our mission, through the creation of custom silicon, hardware and software, is to enable multimodal Generative AI inference acceleration at scale, with safe, sustainable, high-performance systems for our hyperscaler and neocloud data center customers.
We are at the leading edge of advancing the latest research and product improvements for generative Al inference solutions that will make Al even more advantageous for compelling new generative AI applications.
Tensordyne is a well funded, fast-paced startup company with headquarters in both Sunnyvale, CA, and Munich, Germany.
We also have many talented team members working remotely across North America and Europe.
We take care of our people and their families with comprehensive.
Come join us as we shape the future of multimodal generative artificial intelligence! Role summary How much numerical precision does an LLM need, and where in the model architecture does it matter most? When does quantization improve runtime, and what does it cost in model quality? We're looking for a motivated working student to help turn these questions into experiments and experiments into maintainable code.
You'll explore optimization methods and build reusable evaluation tooling.
You'll work closely with our quantization, compiler, and runtime engineers, and your findings will directly influence how we optimize models for Tensordyne hardware.
What you will do Model qu.