Choosing the Right AI Model Shouldn’t Feel Like Guesswork

European enterprises deploy AI applications faster when they can compare models, understand compliance implications, and switch providers without vendor lock-in. GLBNXT's Model Hub centralizes specifications for every available large language model (LLM) on the platform: the foundational and embedding models that power generative AI and machine learning applications. From parameters and context windows to hosting location and licensing, you can confidently select models that match your technical requirements and sovereignty needs.

Why should you have a Model Hub?

Model selection directly impacts application performance, cost, and compliance. Without centralized tooling, teams waste time comparing scattered documentation, testing models blindly, or defaulting to whatever their cloud provider offers. The Model Hub solves this: familiarize yourself with any model's capabilities and assess fit for your specific task before committing resources.

Model Hub informs you about key characteristics

Every model in the Hub links to its original model card, which details the technical specifications: parameter count (e.g., 70B, 120B), context window size (128k, 200k tokens), training cutoff date, and benchmark performance across standard tasks. These specifications matter: parameter count indicates model capability and computational cost, while context window determines how much information the model can process in a single request. We don't just aggregate this information, but we enrich it with hosting location, EU compliance indicators, and integration examples from actual use cases built on our platform.

Model Hub helps you discover models

New models launch monthly, making it difficult to track which ones might outperform your current stack. The Model Hub shows similar models based on capability profiles, training approach, and use case fit. This enables you discover alternatives you didn't know existed. See which models other enterprises use for comparable workflows, explore models from EU providers, or find newer options that match your current model's strengths with better performance or compliance characteristics.

Model Hub helps you select the right model for the task

Different workflow steps demand different models. The Model Hub lets you optimize each stage independently: deploy a large reasoning model for complex analysis, then switch to a faster model for summarization. Use a text-generation specialist for marketing copy, then an image model for visuals. This granular control means you're not locked into a single provider's ecosystem or forced to compromise on performance because one model can't handle every task well.

Model Hub makes you resilient and future proof

Model providers deprecate APIs, change pricing, or restrict access. The Model Hub creates resilience into your AI stack by treating models as interchangeable components. When newer models launch, they're immediately available across the platform. Find comparable alternatives to your current models and swap them out in minutes, not weeks of re-engineering. Your applications stay running regardless of provider decisions.

Characteristics inside the Model Hub

Understanding model characteristics means selecting the right tool for each task. Here's what matters when evaluating models in the Hub.

Parameters (Model Size)

Parameter count measures model complexity. More parameters mean greater capacity to capture patterns, typically resulting in stronger performance on complex tasks but higher computational costs and slower inference. For deep reasoning or specialized knowledge, choose large models (70B+). For speed-critical applications with simpler tasks, smaller models deliver better value. Newer models optimize parameter efficiency, often matching or exceeding older large models with fewer parameters.

Context Window

Context window defines how much information a model can process in a single request, measured in tokens. This includes your system prompt, conversation history, input documents, and the model's responses. Larger context windows (200k+ tokens) enable analysis of lengthy documents or multi-turn conversations, but increase computational costs and inference time. Extended contexts can also cause focus drift, where models lose track of instructions amid excessive information.

Intended Usage

Models are optimized for specific domains through its training data: general knowledge, code generation, multimodal tasks, or specialized fields. Match the model's intended use to your application. A code-specialized model won't perform well for customer support, just as a conversational model will struggle with technical code generation. Check training objectives and data before deployment.

Capabilities & Limitations

Beyond intended use, examine benchmark performance and documented limitations. Benchmarks measure capabilities across standardized tasks, but don't overlook practical constraints: language support matters for EU markets where a model trained primarily on English will underperform in German, French, or Dutch applications. Review known limitations around hallucinations, bias, and refusal behavior to assess deployment risk for your specific use case.

License and hosting

Licensing and hosting location determine data sovereignty and operational flexibility. The Model Hub publishes both for every model. Proprietary models require API access to the provider's infrastructure, with data processing at their discretion. Open-source models can be self-hosted or deployed through EU-based providers, giving you control over data location. We specify exactly where each model runs: provider APIs, EU-hosted aggregators, or our own infrastructure. This transparency lets you make informed decisions about data processing locations and compliance requirements.

GLBNXT Model Hub

The Model Hub gives you the technical depth to evaluate models on specifications and benchmarks, with the practical context to understand real-world fit. Advanced users filter by parameters and capabilities. Teams new to MLOps get curated recommendations based on proven use cases. Either way, you select models with full transparency on performance, cost, and data sovereignty. Without vendor lock-in.

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