Sparsity is an AI model compression technique that can trim model size by 50% with a minimal decrease in performance. CIOs should task their teams with testing sparse models to reduce cloud costs and accelerate inference, especially if edge deployment is on the roadmap.
Why You Should Care
Today’s leading AI models are very large. Models like Llama 3.1 and Mistral Large 2 can exceed 229 GB, making them cost-prohibitive for continuous cloud inference and nearly impossible to deploy on-device. Sparsity offers a smarter, more strategic alternative to brute-force compression. This technique uses structured, unstructured, semi-structured, or block approaches to reduce model size while keeping performance largely intact. Open-source tools (such as SparseGPT, Wanda, and SparseML) make implementation accessible, even skipping the retraining step in many cases. These tools come bundled with performance benchmarks to help IT teams assess the trade-offs with minimal guesswork. If you are looking to tighten the belt, combining sparsity with quantization (another model compression technique) can deliver compounded performance and cost savings.
What You Should Do Next
- Identify high-cost models in production or in development that could benefit from compression.
- Run pilot experiments to test model compression and evaluate results.
- Consider quantization as a second layer of optimization for latency-sensitive applications.
Get Started
- Start with free sparse model repositories like Hugging Face or SparseZoo to trial models with varying sparsity techniques.
- Task your AI team with applying one of the sparsity tools to a production model and evaluate using in-house or provided benchmarks.
- Consider combining sparsity with quantization, especially for mobile or edge deployments where power and compute are limited.
- Integrate sparsity evaluations into your model deployment workflow to monitor accuracy, latency, and throughput as default metrics.