INTRODUCTION
Supercharging Decentralized Computing with Advanced Cloud Solutions
In mid-2022, the innovative team behind opGPU embarked on a mission to address a fundamental issue within the realm of institutional-grade trading systems. Their objective was to construct an infrastructure capable of efficiently supporting ultra-low-latency and high-performance algorithms across both the U.S. equities and cryptocurrency markets. This endeavor required a meticulous approach to designing a robust and scalable platform that could handle the complexities and demands of modern trading environments.
Their vision focused on overcoming the limitations of traditional systems by leveraging cutting-edge technology and advanced computations. By doing so, they aimed to provide traders with the tools required to execute high-frequency trades with unparalleled speed and accuracy, thus giving institutional clients a significant competitive edge. Through continuous innovation and collaboration with industry experts, the opGPU team endeavored to bridge the gap between existing infrastructure and the rapidly evolving
At the heart of our work was high-frequency trading (HFT), an advanced trading strategy that necessitates the continuous real-time analysis of tick data sourced from over 1,000 different stocks and an additional 150 cryptocurrencies. Our primary goal with this endeavor is to efficiently execute trades on behalf of our 30,000+ clients. These trades occur across a variety of platforms, including but not limited to, E*TRADE, Alpaca, and Binance. A crucial factor for success in this domain is maintaining extremely low latency, which must consistently be kept under 200 milliseconds. By achieving this, we can ensure precise and timely transaction processing that meets the high demands of our diverse clientele, positioning our operations at the cutting edge of financial technology
But the challenge wasn’t just about speed. Our system needed to dynamically backtest and optimize algorithms for each asset, adjusting in real-time to ever-shifting market conditions. This required massive computational power, and the traditional route would have meant assembling an army of MLOps and DevOps engineers to manually build and manage the infrastructure.
Instead, we turned to Ray, the open-source distributed computing framework used by OpenAI to scale training for models like GPT-3/4 across hundreds of thousands of CPUs and GPUs. By integrating Ray into our backend, we were able to streamline our architecture, reducing our build time from 6+ months to less than 60 days.
But even with this breakthrough, a new challenge emerged: costs.
To handle the scale of our workloads, we required over 50 NVIDIA A100s, each priced at $80/day. Running this at full capacity for 25 days each month pushed our infrastructure costs to $100,000/month—a barrier that proved unsustainable, not just for us but for many self-funded AI/ML startups facing the same issue.
What made this problem even more urgent was the rapidly growing demand for GPU compute. With AI workloads doubling every 3 months and scaling 10x every 18 months, the need for a more cost-effective, accessible solution became clear.
And that’s when opGPU was born: a decentralized GPU network designed to solve the compute cost crisis while enabling the next wave of AI and blockchain applications. Our journey from challenge to solution is just beginning.
OpGPU presents a decentralized approach to GPU solutions, leveraging blockchain technology to provide a secure, adaptable, and cost-effective alternative. Accessing advanced Graphics Processing Units (GPUs) is often essential for modern computationally demanding tasks. Traditional cloud providers typically offer centralized GPU resources, raising concerns about cost, security, and potential vendor lock-in. GPUs play a vital role in accelerating complex computational operations within the decentralized computing sphere. OpGPU is committed to delivering premium GPU solutions to fuel the upcoming surge of decentralized apps (dApps) and services, recognizing the transformative potential of GPUs.
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