AI model and compute speed: the new alpha for quantitative trading
Traditionally, quant researchers focused on building the best-performing models and then handed them off to platform engineers to optimize for latency and cost in production. But at the scale of AI, that separation no longer works. Modern quant firms need to move faster, enabling teams to build the best models and optimize them on the fastest infrastructure simultaneously. Also adding to the complexity is the fact that existing model-building tools weren’t designed for the massive size of today’s models.
A new tech stack is needed to streamline the quantitative research loop from initial hypothesis to production, helping firms discover strategies faster and deploy models with confidence.
Download this ebook to learn:
- Emerging trends shaping the future of quant trading
- How training LLMs is inspiring innovation in the latest quant models
- The key components of a modern AI technology stack
- Why achieving higher Model FLOPs Utilization (MFU) and faster learning velocity is critical
- Strategies to build and train higher quality AI models using advanced techniques
Deliver higher-performing models on optimized compute. Download the ebook today.
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