Positron AI Secures $51.6 Mn in Series A to Build its AI Inference Engine

Positron AI, known for developing American-made hardware and software for AI inference, has bagged a $51.6 million oversubscribed Series A funding round, raising its total capital for the year to over $75 million.

The round was led by Valor Equity Partners, Atreides Management, and DFJ Growth, with additional investments from Flume Ventures, Resilience Reserve, 1517 Fund, and Unless.

This funding will go towards the deployment of Positron's first product, Atlas, and accelerate the launch of its second-generation products in 2026. 

With global tech firms projected to spend over $320 billion on AI infrastructure by 2025, businesses are facing rising cost pressures, power limits, and shortages of NVIDIA GPUs. Positron’s specialised solution provides cost and efficiency advantages.

Positron claims that Atlas offers 3.5 times better performance per dollar and up to 66% lower power consumption compared to NVIDIA’s H100. Atlas is specifically designed to enhance generative AI applications.

“By generating 3x more tokens per watt than existing GPUs, Positron multiplies the revenue potential of data centres. Positron’s innovative approach to AI inference chip and memory architecture removes existing bottlenecks on performance and democratizes access to the world’s information and knowledge," said Randy Glein, co-founder and managing partner at DFJ Growth. 

Positron Atlas boasts a memory-optimised FPGA architecture that achieves 93% bandwidth utilisation, far exceeding the typical 10–30% of GPU systems and supports models with up to half a trillion parameters in a single 2-kilowatt server. 

It is also compatible with Hugging Face transformer models and processes inference requests through an OpenAI API-compatible endpoint. Powered by US-made chips, Atlas is already in use for LLM hosting, generative agents, and enterprise copilots, offering lower latency and reduced hardware demands.

“Our highly optimised silicon and memory architecture allows for superintelligence to be run in a single system with our target of running up to 16-trillion-parameter models per system on models with tens of millions of tokens of context length or memory-intensive video generation models,” said Mitesh Agrawal, CEO of Positron AI. 

With the series A funding secured, Positron is advancing its next-gen system for large-scale frontier model inference. Titan, the successor to Atlas and powered by Positron’s 'Asimov' silicon, will support up to two terabytes of high-speed memory per accelerator, enabling the handling of 16-trillion-parameter models and significantly expanding context limits for the largest models.