TensorOpera and Aethir Collaborate to Advance LLM Training on Decentralized Cloud

  • TensorOpera partners with Aethir to enhance TensorOpera Fox-1, a cutting-edge SLM with 1.6 billion parameters.
  • Fox-1 outperforms competitors in LLM benchmarks despite fewer parameters, thanks to its innovative training approach.
  • Aethir provides decentralized GPU resources crucial for scalable and cost-effective AI model development.
  • Integration of Aethir’s infrastructure into TensorOpera’s platform supports dynamic scaling and high-performance AI applications.
  • Collaboration aims to empower developers with enhanced resources for pioneering AI technologies.

Main AI News:

In a strategic move aimed at pushing the boundaries of large language model (LLM) development, TensorOpera has teamed up with Aethir, a prominent distributed cloud infrastructure provider. The collaboration focuses on enhancing TensorOpera’s latest innovation, TensorOpera Fox-1, heralded as a groundbreaking open-source small language model (SLM) boasting 1.6 billion parameters.

Introduced recently, TensorOpera Fox-1 stands out for its unparalleled performance among models in its category, surpassing offerings from tech giants like Apple, Google, and Alibaba. This decoder-only transformer model was meticulously trained from scratch on a staggering three trillion tokens, employing a novel 3-stage curriculum. Its architecture, notably 78% deeper than comparable models such as Google’s Gemma 2B, excels in standard LLM benchmarks like GSM8k and MMLU despite having significantly fewer parameters.

Central to the partnership is Aethir’s provision of advanced GPU resources crucial for scaling the training of TensorOpera Fox-1. Leveraging Aethir’s expansive decentralized cloud infrastructure, which includes partnerships with NVIDIA Cloud Partners and enterprise-grade hardware providers, TensorOpera gains access to cost-effective and scalable GPU resources. These resources are essential for high-throughput processing, substantial memory capacity, and efficient parallel processing capabilities required in AI model development.

Salman Avestimehr, Co-Founder and CEO of TensorOpera, expressed enthusiasm about the collaboration, highlighting Aethir’s decentralized infrastructure as pivotal in supporting dynamic AI model scaling. He emphasized the flexibility and performance benefits witnessed during the Fox-1 model’s training, prompting TensorOpera to integrate Aethir’s GPU resources directly into its AI platform. This integration aims to empower developers with the necessary tools to innovate in AI technologies effectively.

Aethir, known for its globally distributed network of high-performance GPUs tailored for enterprise AI and machine learning applications, underscores its commitment to advancing AI capabilities through decentralized infrastructure. By dispersing GPU resources across numerous smaller clusters rather than consolidating them in large data centers, Aethir ensures low latency and scalability, crucial for demanding AI workloads worldwide.

Kyle Okamoto, CTO of Aethir, emphasized the strategic importance of the collaboration with TensorOpera, positioning Aethir as a key supplier of enterprise GPU infrastructure for cutting-edge AI platforms. Daniel Wang, CEO of Aethir, echoed this sentiment, highlighting their capability to support large-scale AI development and deployment globally through their robust decentralized cloud infrastructure.

The partnership between TensorOpera and Aethir marks a significant step forward in advancing LLM technology, promising to empower AI developers with unprecedented resources for creating and deploying next-generation AI applications.


The partnership between TensorOpera and Aethir signifies a significant advancement in the AI market, leveraging decentralized cloud infrastructure to meet the escalating demand for high-performance AI training and deployment capabilities. This collaboration not only enhances the scalability and efficiency of AI model development but also underscores the importance of robust, distributed GPU resources in driving future innovations across industries reliant on AI technologies.