GPUs (graphics processing units) are a type of computing hardware which can be used to perform a series of computations in parallel. This makes them useful in graphics applications, however, they have since become a crucial tool within machine learning. Used in both training and inference, they have significantly accelerated training times for deep learning models, enabling the development of state-of-the-art AI systems.

However, there are a number of challenges associated with GPU usage in machine learning:

     1) High costs: High-performance GPUs can be expensive, making them a significant investment for organizations and individuals. This cost can be a barrier for smaller projects or researchers with limited budgets. There has also been NVIDIA GPU shortages, pushing lead times up significantly, and increasing GPU prices further

     2) Compatability: GPUs require compatible hardware and software. Ensuring that your machine learning framework and libraries are GPU-accelerated and that your GPU is compatible with these tools can be a challenge

     3) Energy usage: GPUs consume a substantial amount of power, leading to increased energy costs for running machine learning workloads on GPU servers or personal machines. This is a concern for both environmental and economic reasons

     4) Parallelism:  Whilst GPUs excel at parallel processing, not all machine learning algorithms are highly parallelizable. Some tasks may not benefit significantly from GPU acceleration, making it important to choose the right hardware for the job

     5) Memory constraints: GPUs have limited memory compared to traditional CPUs. This can become problematic when working with large datasets or deep learning models which require significant memory capacity.

     6) Driver and software updates: GPU drivers and software libraries must be kept up to date for optimal performance and compatibility. This maintenance can be time-consuming and therefore costly.

     7) Portability: GPUs are typically found in desktop workstations or specialized servers. Deploying GPU-based machine learning models in resource-constrained environments or on edge devices can be challenging due to their size, power consumption, and cost.

     8) Vendor lock-ins: Different GPU vendors (e.g., NVIDIA, AMD) may have specific tools and libraries, leading to vendor lock-in concerns. This can limit flexibility and interoperability in the long term.

The Titan Takeoff Inference Server offers solutions to these challenges associated with GPU usage.