Compute Cost Optimization

Benched.ai Editorial Team

Compute cost optimization focuses on reducing the dollar spend required to train or serve AI models while meeting performance and reliability goals.

Cost Levers

Lever	Training Impact	Inference Impact
Mixed precision (FP16/FP8)	2–3× speedup on tensor cores	Halves memory footprint
Quantization	Not common during full training	2–4× throughput, 50 % lower GPU hours
GPU Spot Instances	40–80 % cheaper but pre-emptible	Rarely used for prod inference
Batch size tuning	Improves GPU utilization	Raises latency if overdone
Checkpoint sharding	Saves SSD and network I/O	Neutral

Optimization Workflow

Profile baseline GPU utilization, memory, and latency.
Identify bottleneck (compute vs memory vs network).
Apply single change and measure cost per token.
Iterate; stop when marginal savings < effort.

Design Trade-offs

Spot capacity interruptions can waste progress unless training supports fault tolerance.
Aggressive quantization may drop accuracy below SLA.
Larger batches save cost but require bigger context windows and may hit divergence in training.

Current Trends (2025)

Token-based billing models tie cost directly to output; optimizing prompt length now yields 20 % savings.
GPU cooperatives share idle capacity across companies via secure multi-tenancy.
Carbon-aware schedulers shift non-urgent jobs to low-tariff renewable time slots¹.

Implementation Tips

Track cost per thousand tokens (CPT) alongside latency.
Negotiate committed-use discounts for predictable baseline usage.
Archive old checkpoints to cold storage tiers.

Microsoft Azure, Carbon-Aware AI Job Scheduling Whitepaper, 2025. ↩