Ampere GPUs have improved throughput and throughput-per-dollar compared to pre-Ampere generation GPUs, with significant benefits for language models. The Ampere GPU family offers better performance per dollar than Turing/Volta generation GPUs. However, the lower-end GPUs in the Ampere family may be more cost-effective options when considering budget constraints. Scalability tests showed that some Ampere GPUs perform well with multi-GPU training jobs, while others, such as Geforce cards, experience significant bottlenecks. The recommended GPU choices for Deep Learning depend on specific needs, including multi-node distributed training and model size, with the A100 80GB SXM4 being a top choice for large models and A6000 for mainstream research.