Gen AI Tools & Architecture
Generative AI is a field of active research and development. LLMs, RAG architectures, and related tools are new and rapidly changing. The tools you use are almost as important as the problems you choose to solve. Not only for managing accuracy but because the value you create might be overcome by the cost of the solution. Understanding how to leverage your current investments in ML, data, and cloud against tradeoffs in performance is key. The choices are complex, but Ippon has pulled the leading options in the market together into accelerators that help you make the right choice for your business and project. Using our project accelerators, we can lay out your options and help make the selections that best suit your organizational needs.
Challenges
- Constantly Evolving Technology: New models and tools for generative AI are emerging rapidly, making it difficult to keep pace.
- Performance vs. Cost Tradeoffs: Finding the right balance between model performance and operational costs can be challenging.
- Customizing Large Language Models (LLMs): Implementing and fine-tuning LLMs for specific use cases requires deep technical expertise.
- Integration of Generative AI with Existing Systems: Organizations struggle to integrate new generative AI capabilities with their existing workflows and data pipelines.
Benefits
Expert Tool Selection
We evaluate generative AI tools tailored to your specific use cases, platforms, budget, and performance requirements to ensure the best fit for your organization.
Optimized Architecture Design
Our team designs scalable and efficient architectures that meet your data volume, model complexity, and performance needs while ensuring seamless integration with your existing infrastructure.
Performance & Resource Optimization
Using advanced techniques like model compression, quantization, and hardware acceleration, we optimize the performance of your AI models while allocating computational resources efficiently to minimize costs.
Cost Management Strategies
We leverage cloud-based infrastructure and perform cost analyses to manage expenses across different LLMs and resource usage, ensuring long-term sustainability without compromising performance.
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