Analysis
This article provides a compelling early benchmark of Google DeepMind's newly released Gemma 4, demonstrating its significant superiority over the established Qwen 3.5 in practical financial tasks. It highlights a major efficiency breakthrough where the MoE (Mixture of Experts) version achieves identical accuracy to the Dense model while running nearly three times faster and using less VRAM, making high-performance local AI more accessible than ever.
Key Takeaways
- •Gemma 4 achieved 88% accuracy in stock prediction tasks compared to Qwen 3.5's 71%, reducing false signals from 19 to just 4.
- •The MoE (26b) version matches the Dense (31b) model's output 100% identically while offering 2.9x faster inference and lower hardware requirements.
- •The model demonstrates superior judgment in distinguishing between routine disclosures and material market-moving news, a critical feature for financial applications.
Reference / Citation
View Original"Gemma 4 is superior to Qwen 3.5 in all metrics: accuracy, speed, and VRAM efficiency. Specifically, the MoE version (26b) showed an ideal balance for practical deployment—fastest speed and lowest VRAM usage without dropping accuracy."
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