Matching Your Workload to the Right Configuration
AI model training (small to medium models)
Look at AMD EPYC 7642 + NVIDIA L4 24GB configurations. The 48-core EPYC provides strong data preprocessing throughput alongside the GPU, and 128GB RAM prevents system memory from becoming a bottleneck during data loading.
Production ML inference at scale
Intel Xeon Gold 6152 + NVIDIA Tesla T4 16GB in Toronto is a well-optimized inference platform. T4's INT8 precision support through TensorRT makes it particularly efficient for serving NLP, image classification, and recommendation models in production.
3D rendering and visual effects
Dual-processor systems with multiple GPUs (such as 2x E5-2697 v4 with 2x Tesla M40) provide the parallel processing headroom that offline rendering pipelines require. 256GB RAM accommodates large scene files.
Development and testing environments
Entry-level configurations with Tesla P4 or T1000 8GB GPUs, starting from $133/month, are appropriate for development workloads, CI/CD pipelines that include GPU testing, and smaller inference services.
Data-heavy analytics with GPU acceleration
Systems with large RAM pools (192–256GB) and 1Gbps+ unmetered bandwidth support workloads that move significant data volumes between storage, memory, and GPU.