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Select efficient ML frameworks and inference runtimes

Applicable Role: Provider and Consumer

Description​

Machine learning frameworks and inference runtimes are the core execution engines for AI and ML workloads. These tools determine how efficiently models and algorithms utilize available hardware, manage memory, and optimize compute across CPU, GPU, TPU, and specialized accelerators.

Different frameworks and runtimes vary significantly in their ability to leverage hardware capabilities, execute operations efficiently, and minimize computational overhead. Inefficient framework choices can lead to unnecessary compute consumption, poor hardware utilization, and increased energy expenditure for the same workload.

Selecting efficient ML frameworks and inference runtimes improves model execution performance and reduces the carbon footprint of AI training and inference.

Solution​

  • Choose frameworks that efficiently utilize available hardware (GPUs, TPUs, specialized accelerators)
  • Prefer frameworks with native support for hardware acceleration and parallel processing
  • Evaluate inference runtimes (ONNX Runtime, TensorRT, OpenVINO) that are optimized for model execution
  • Use optimized inference layers that reduce latency and compute overhead compared to training frameworks
  • Select frameworks with strong compiler optimization and memory management capabilities
  • Benchmark framework options under your actual workload conditions before committing to production
  • Keep frameworks and runtime dependencies updated to benefit from performance and efficiency improvements
  • Consider compilation frameworks (ONNX, OpenVINO) that optimize models for specific hardware targets

SCI Impact​

SCI = (E × I) + M per R

E (Energy): Efficient framework selection, optimized hardware utilization, and reduced latency directly lower energy consumption per inference or training operation.

M (Embodied Carbon): Improved hardware utilization can reduce the need for additional infrastructure and associated embodied emissions.

Cost Impact​

  • Compute costs: Reduced through efficient execution and better hardware utilization; faster inference reduces per-operation cost
  • Development costs: May increase due to framework migration or retraining teams on new tools
  • Infrastructure costs: Lower due to improved utilization and reduced resource requirements
  • Licensing costs: Framework and runtime licensing vary by choice (most open-source options are free)
  • Trade-off: Long-term compute savings must be weighed against upfront engineering investment and team ramp time

Assumptions​

  • Selected frameworks and runtimes are compatible with application requirements
  • Performance benchmarks reflect real-world workload behavior and hardware configurations
  • Team has capacity to evaluate and learn new frameworks if migration is needed

Considerations​

  • Framework migration may require significant effort and refactoring of existing code
  • Compatibility with existing tools, libraries, and pipelines must be evaluated
  • Some optimized runtimes may be hardware-specific (NVIDIA TensorRT, Apple Metal)
  • Training framework efficiency may differ from inference runtime efficiency; choose accordingly for your use case
  • Framework maturity and community support should factor into the decision
  • Performance gains must be validated under actual workload conditions, not just benchmarks
  • Some specialized frameworks may have limited ecosystem or third-party library support

References​