PH Deck

Users face a situation where determining if their hardware can support running a specific LLM model is challenging.

The old solution involves manually checking hardware specifications and compatibility issues with LLM models.

The drawbacks include the time-consuming and potentially confusing process of assessing compatibility individually for each model and hardware setup.

Users struggle to manually research and compare LLM models across multiple sources, leading to inefficiency, difficulty in identifying optimal models, and potential mismatches for their projects.

Users struggle to test language model (LLM) prompts and configurations efficiently, facing slow testing processes and difficulty comparing results side-by-side.

Architects, real estate developers, and urban planners manually create physical scale models for presentations, which is time-consuming, resource-intensive, and requires specialized craftsmanship.

IT teams and developers currently rely on traditional tools and methods to benchmark and optimize Local LLMs (Large Language Models), which lack precise benchmarks and standardized performance measurement metrics across different hardware setups.

Decision-makers face difficulty in choosing the appropriate hardware to deploy LLMs due to insufficient data-driven insights.

Users exploring the capabilities of Large Language Models (LLMs) often face difficulty in coming up with effective prompts to fully leverage these models' potential. This leads to suboptimal usage and experimentation, hindering learning and application in various contexts. The drawbacks include a lack of inspiration, inefficiencies in understanding the full capabilities of LLMs, and an inability to apply these models effectively across different domains.

Designers, brands, and e-commerce businesses struggle with creating lifelike digital fashion models for showcasing clothing and designs.

Users seeking detailed and realistic representations of their designs or ideas through physical models often rely on traditional model making techniques, which can be time-consuming, expensive, and limited in complexity and precision.

Runners struggle to track their routes and capture essential statistics with traditional running apps, leading to less accurate or engaging run tracking experiences.