The focal point of enterprise AI strategy is rapidly shifting from 'single-model dependency' to 'multi-model combinations.' Google Cloud has solidified this trend by integrating thousands of open-source large language models (LLMs) into its Vertex AI Model Garden. Enterprises have now entered an era of 'AI shopping,' where they can select models optimized for their specific business purposes rather than being confined to the closed ecosystem of a specific model provider.

Unifying a Fragmented Model Ecosystem

Through Vertex AI Model Garden, Google Cloud has built a massive ecosystem that encompasses not only Google’s first-party models but also Meta's Llama 3.2, Mistral AI's Mistral Large 2 and Mistral Small 3.1 24B, and Google’s open models such as Gemma 3 and TranslateGemma. This goes beyond a simple listing of models, representing a fusion of performance and stability required by enterprise environments.

Specific performance metrics support this expansion. Mistral Small 3.1 24B, included in Model Garden, demonstrated efficiency by recording 81.0% on the Massive Multitask Language Understanding (MMLU) benchmark. For enterprises where coding capabilities are critical, Mistral Large 2, which recorded a Python accuracy of 92.1%, serves as a viable alternative. Global companies requiring multilingual services can choose TranslateGemma, which specializes in text extraction from images and translation.

The approach is intuitive. Enterprises utilize a 'Model-as-a-Service (MaaS)' environment that allows for immediate model deployment without the burden of infrastructure management. By linking dedicated TPU (Tensor Processing Unit) resources and A3 VM high-performance infrastructure, Google has elevated the potential of open models to enterprise-grade services.

Technical Optimization: Speed and Efficiency

Sophisticated technical support is essential for managing tens of thousands of models. Google has ported vLLM framework technologies, such as PagedAttention and Continuous Batching, into Vertex AI. These technologies reduce memory waste during the inference process and maximize throughput, enabling open models to run smoothly even in latency-sensitive real-time services.

Quantization techniques to lower memory occupancy have also been actively introduced. Techniques such as AWQ (Activation-aware Weight Quantization) and GPTQ (Gradient-based Post-Training Quantization) have been applied to reduce GPU memory usage. This allows enterprises to operate high-performance models with fewer hardware resources, ensuring cost-efficiency. Additionally, the auto-scaling feature of managed endpoints provides stability without service interruption, even during sudden traffic spikes.

The differentiators from other platforms lie in 'data integration' and 'evaluation.' Vertex AI Model Garden is natively integrated with BigQuery, providing a single workflow where enterprises can immediately utilize vast amounts of proprietary data for model training and analysis. Furthermore, through the 'Generative AI Evaluation Service,' companies can use objective figures to compare and analyze which open model performs best on their specific data.

Strategic Inflection Point: Model Sovereignty vs. Fragmentation

This ecosystem expansion is significant in that it grants 'model sovereignty' to enterprises. It enables multi-model strategies where companies can replace or use models in combination as needed, without being swayed by the policy changes or API price hikes of a specific AI vendor. This serves as a powerful selling point for CTOs wary of technical lock-in.

However, challenges remain. As the number of models in the Model Garden exceeds 35,000, there are concerns about 'choice overload.' Since training data and biases vary by model, controlling these across the enterprise and maintaining governance will become a new task for companies. Additionally, it remains to be seen whether the optimization with Google's proprietary hardware (TPU) can consistently keep pace with the update speed of the open-source community.

Strategies for Immediate Execution

AI leads in enterprises must move beyond the simple question of "Which model is the best?" Instead, they should consider "Which combination of models is the most cost-effective for our specific workload characteristics?"

1. Workload-specific Model Mapping: Build a portfolio by deploying the Mistral series for low-latency real-time customer responses, Mistral Large 2 for complex data analysis and coding assistance, and Llama 3.2 or TranslateGemma for global content generation.
2. Utilize Evaluation Services: Rather than relying on subjective judgment, use Vertex AI’s evaluation tools to regularly perform benchmarks for each model against domain-specific data.
3. Integrate Data Pipelines: Use model management tools linked with BigQuery to automate the process from data collection to model deployment, thereby reducing operational overhead.

FAQ

Q1: Is the performance of the open models in Model Garden reliable? A: Many models have already proven top-tier performance in major benchmarks, such as Mistral Small 3.1 24B recording 81.0% on MMLU and Mistral Large 2 showing 92.1% accuracy in Python coding. However, performance within specific industries or on internal corporate data should be separately verified through the Vertex AI Evaluation Service.

Q2: Does deploying and managing so many models incur high infrastructure costs? A: Google Cloud minimizes the burden of infrastructure management by providing a MaaS environment. Furthermore, efficiency is achieved by lowering GPU memory occupancy through quantization techniques like AWQ and GPTQ, and by paying only for resources actually used through the auto-scaling feature.

Q3: What are the unique strengths of Vertex AI compared to model services on other cloud platforms? A: The core strengths are high-speed inference utilizing Google's proprietary TPU hardware and powerful data integration with BigQuery. Moreover, it differentiates itself by providing a 'Generative AI Evaluation Service' within a single platform, allowing enterprises to objectively compare models rather than just providing them.

Conclusion

The expansion of Google Cloud’s Vertex AI Model Garden symbolizes the shift in the AI market's center of gravity from the models themselves to the 'platform and ecosystem.' The competitive edge no longer depends solely on who creates the smartest model, but on who can best orchestrate tens of thousands of models to suit enterprise needs. Enterprises must now move beyond passively accepting given models and build their own AI competitiveness through multi-model strategies.

참고 자료

• 🛡️ Gemma 2 27B vs Mistral Small 3.1 24B Base - LLM Stats
• 🛡️ Comprehensive Review of the Llama 3.1 and Mistral Large 2 Models
• 🛡️ Quantize LLaMA2 models with AWQ or GPTQ and deploy on vLLM
• 🛡️ AWS SageMaker vs Google Vertex AI: The Complete 2025 Comparison
• 🛡️ Google Cloud's Vertex AI Model Garden Enhances Business Agility
• 🏛️ google/translategemma-27b-it - Hugging Face
• 🏛️ Serving open-source large language models efficiently on Vertex AI Model Garden
• 🏛️ Vertex AI Platform | Google Cloud
• 🏛️ Vertex AI Platform | Google Cloud