Choosing the right GPU for machine learning and large language model (LLM) workloads is crucial, with NVIDIA's A10 and L40S GPUs offering distinct advantages. The A10, based on NVIDIA’s Ampere architecture, is cost-effective and suitable for mid-sized AI inference and graphics-heavy tasks, while the L40S, leveraging the Ada Lovelace architecture, provides superior performance for demanding AI and graphics workloads due to its higher CUDA core count, greater VRAM, and better memory bandwidth. Performance benchmarks reveal that the L40S consistently outperforms the A10 in latency and throughput across various AI models, although it comes at a higher cost. With the global GPU shortage posing a challenge, Clarifai’s Compute Orchestration allows businesses to access both A10 and L40S GPUs flexibly across multiple cloud providers, avoiding vendor lock-in and optimizing for availability, performance, and cost. This flexibility is crucial for scaling AI projects efficiently, ensuring that organizations can choose the most appropriate GPU based on their specific needs and budget constraints.