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GPU vs CPU — Choosing the right compute for your workload
This page explains typical workload patterns and which SolarCompute offering is a better fit. It is intended to help developers and commercial customers choose between GPU compute (optimised for ML and model workloads) and CPU / batch compute (optimised for throughput and general-purpose tasks).
GPU Compute
Use GPU instances when your workload benefits from parallel floating‑point acceleration. Common use-cases:
- Training and fine-tuning machine learning models (large matrix and tensor operations).
- Accelerated inference for deep learning models, particularly when latency and throughput per model matter.
- High-performance rendering workloads that leverage GPU rasterisation or ray tracing libraries.
- Workloads that use frameworks such as PyTorch, TensorFlow, or CUDA-enabled libraries.
When to prefer GPU:
- Your task is heavily parallel and benefits from many cores and fast on‑device memory.
- You need reduced wall-clock time for model training or batched inference.
CPU & Batch Compute
Use CPU-optimised instances for general-purpose compute, IO-bound tasks, and throughput-oriented batch processing. Common use-cases:
- Data preprocessing, ETL, and CPU-bound model inference for small models.
- Rendering or video encoding pipelines that are not GPU-accelerated.
- Large-scale batch jobs where many parallel CPU workers provide better cost or utilisation characteristics.
- Stateless microservices, build systems, and general compute tasks.
When to prefer CPU:
- The workload is IO-bound, single-threaded, or does not leverage GPU libraries.
- You prioritise throughput across many independent tasks rather than per-job execution time.
Practical guidance
- Prototype on a small GPU instance to validate model behaviour, then scale horizontally with CPU workers for preprocessing and orchestration.
- Measure wall-clock time and cost per useful unit (training epoch, rendered frame, inference per 1k requests) — choose the instance type that minimises your total cost for the required SLA.
- For hybrid pipelines, split tasks: use GPU for model compute and CPU for data transformation, scheduling jobs where energy availability and cost align with your requirements.
Ready to run workloads on SolarCompute?
Request a commercial feasibility quote or contact our developer team to discuss machine specifications and deployment options.
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Developer Docs
This page provides guidance only. For specific machine specifications and availability, contact hello@solarcompute.net.au.