Great for training and fine-tuning medium to large AI models with a balance of power and cost.

Best for large-scale model training and distributed compute workloads like LLMs, diffusion models, etc.

Slight performance uplift over H100; great for high-throughput inference or efficient fine-tuning.

Solid choice for enterprise-grade setup for generative models and intensive compute pipelines.

Entry-level GPU; ideal for testing, small inference jobs, or development environments.

Affordable choice for batch inference or lightweight training with moderate memory needs.

Solid middle-ground for model tuning and image generation with modest GPU memory needs.

Fully unlocked instance for high-parallelism workloads; great for multi-container pipelines.

Suitable for classic deep learning models, moderate batch size training, or experimentation.

Powerful setup for training medium-to-large models and running intensive parallel tasks.

Top-tier legacy compute; best for larger models, multi-GPU training, or fast inference at scale.

Single A10G (24GB); great for small finetunes, LoRA tests, and quick protos.

1×A10G with more CPU/RAM; smoother data prep + medium-size inference.

1×A10G, ample RAM; stable larger-batch inference and preprocessing.

High-RAM single GPU for memory-heavy finetunes, embeddings, video/image pipelines.

4×A10G; multi-GPU training, distributed inference, parallel batch generation.

Maxed single-GPU node; big-RAM ETL + training where 1 GPU is enough.

4×A10G with huge RAM; bigger-context finetunes and heavy data pipelines.

8×A10G in one box; data-parallel training or very high-throughput inference.