vastai-cost-tuning

# Vast.ai Cost Tuning ## Overview Minimize Vast.ai GPU cloud costs by choosing the right GPU for your workload, leveraging interruptible (spot) instances, eliminating idle compute, and implementing auto-destroy safeguards. Vast.ai pricing is dynamic and varies significantly: RTX 4090 ($0.15-0.30/hr), A100 80GB ($1.00-2.00/hr), H100 SXM ($2.50-4.00/hr). ## Prerequisites - Vast.ai account with billing history - Understanding of your workload's GPU requirements - `vastai` CLI installed ## Instructions ### Step 1: GPU Selection by Cost-Efficiency ```python # Compare cost-per-TFLOP across GPU types GPU_SPECS = { "RTX_4090": {"fp16_tflops": 82.6, "vram": 24}, "A100": {"fp16_tflops": 77.97, "vram": 80}, "H100_SXM": {"fp16_tflops": 267, "vram": 80}, "RTX_3090": {"fp16_tflops": 35.6, "vram": 24}, "A6000": {"fp16_tflops": 38.7, "vram": 48}, } def cost_per_tflop(gpu_name, dph): specs = GPU_SPECS.get(gpu_name, {"fp16_tflops": 1}) return dph / specs["fp16_tflops"] # Often RTX 4090 is the best value for inference # A100 is best for training large models needing >24GB VRAM # H100 is best only when wall-clock time justifies 10x price premium ``` ### Step 2: Spot vs On-Demand Analysis ```bash # Interruptible (spot) instances are 30-60% cheaper vastai search offers 'num_gpus=1 gpu_name=RTX_4090 rentable=true' \ --order dph_total --limit 5 # Compare interruptible vs on-demand pricing # Use interruptible for: batch inference, checkpointed t...