This use case evaluates the potential of GROQ’s high-performance, energy-efficient hardware to reduce the environmental footprint of AI model execution in Data Exploitation Platforms (DEPs). It compares GROQ’s performance and energy use to traditional GPU systems, offering insights for future DEP deployments that are both powerful and sustainable.
Green Computing Improvement
Challenge
GROQ is a hardware and software platform featuring a Language Processing Unit (LPU) that outperforms traditional GPUs in matrix-based computations. Its large on-chip memory (230MB) enables 10–100× better performance for many AI workloads. Integrating GROQ into DEP operations could significantly accelerate AI model execution while reducing energy consumption—contributing to a greener, more sustainable DEP lifecycle.
Methodology
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Phase 1: Test GROQ cards in a small cluster (e.g. 4-node GPU equivalent setup) to assess baseline performance.
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Phase 2: Run large-scale tests using GROQ’s cloud API and data centre infrastructure to measure performance at scale.
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Focus will be on inference efficiency and energy consumption metrics, benchmarked against traditional GPU-based approaches.
Goal
To evaluate the performance improvements and energy savings that GROQ cards can deliver compared to GPUs, specifically in the context of running AI inference workloads within DEPs.
Relevance
Future DEP operators and developers of AI models who aim to optimise performance while reducing environmental impact. Particularly relevant for those planning large-scale or long-term DEP deployments with high computational demands.
Partners Involved
