Mixtral 8x7B vs LLaMA 2: Why Sparse AI Models Outperform Dense Giants in Real-World Wealth Decisions

Author(s): R. Thompson (PhD)

Originally published on Towards AI.

Imagine asking a question like, “What’s the tax hit if I sell Tesla stock held for 18 months?” and getting an answer that blends IRS logic, stock market trends, and personalized advice in seconds. Welcome to the world of Mixture of Experts (MoE) multi-agent orchestration, a next-gen AI approach revolutionizing financial advisory.

Forget the single-model paradigm. This is about orchestration — LLMs acting like an elite team: a tax expert, a crypto analyst, a storyteller, all responding in harmony.

Let’s explore how.

Financial questions are anything but uniform. They cover corporate earnings, tokenomics, local and national tax law, and macroeconomic narratives. General-purpose models lack deep expertise across domains. We need AI built for specialization — agents trained on specific verticals.

According to a 2023 CFA Institute report, 68% of retail investors make suboptimal decisions due to inadequate or non-personalized financial advice. Add to that the U.S. tax code’s 70,000 pages and the $800B to $3T swings in crypto market cap, and the demand for targeted AI becomes undeniable.

MoE systems activate only the subset of a model needed for a task. Unlike monolithic AI, they scale efficiently. For example, Mixtral 8x7B has 47B parameters but only 13B activate per query, outperforming denser peers like Llama… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI