This content originally appeared on DEV Community and was authored by Deepak Gupta
TL;DR
Explore the architectural differences and hands-on trade-offs between CPUs, GPUs, NPUs, and TPUs. Get actionable insight for making informed processor selections—without code, but with deep technical focus on hardware, ML projects, and system design.
Introduction: Why Processor Choice Matters
As a developer architecting machine learning or edge applications, selecting the right processing unit isn’t just a question of raw performance—it impacts system cost, energy efficiency, scalability, latency, and even privacy.
Get it wrong: and you’ll face bottlenecks, ballooning costs, or frustrated end users.
Get it right: and your ML systems fly, edge inference is seamless, and cloud spend is under control.
Understanding when to deploy CPUs, GPUs, NPUs, or TPUs is now as central to software engineering as framework selection.
Key Factors in Processing Unit Selection
Key criteria developers and architects must consider:
- Workload parallelism: Are you processing millions of independent calculations, or running complex, branching control flows?
- Target environment: Edge/mobile, desktop/server, or cloud-scale.
- Performance vs. power trade-offs: Real-time, high-throughput, or energy-constrained scenarios.
- Ecosystem/tooling: Compatibility with ML frameworks and deployment pipelines.
CPU: The Flexible Generalist
Technical Architecture
- Core count: From dual-core in microcontrollers to dozens in server CPUs.
- Cache hierarchy: Multi-level (L1–L3), key for memory-intensive operations.
- Instruction set: x86, ARM, and RISC-V are common.
- Strength: Handles diverse branching, control-heavy workloads with strong single-threaded performance.
Best Use Cases & Limitations
- Best for: Operating system logic; user input handling; workloads with high branching or conditional paths.
- Limitations: Lags on highly parallel compute workloads (such as neural net matrix multiplication), leading to bandwidth and concurrency bottlenecks.
GPU: The Parallel Processing Powerhouse
Architectural Highlights
- Thousands of SIMD cores: Designed for massive data-parallel operations.
- High memory bandwidth: Enables real-time processing of large datasets.
- Common APIs: CUDA (NVIDIA), ROCm (AMD), OpenCL.
- Strength: ML training/inference, video processing, scientific workloads.
Technical Challenges & Common Workloads
- Shine on tasks where the same operation can happen in parallel on many data points.
- Struggle on branch-heavy, sequential, or low-parallel workloads.
- Optimize for performance with batching and memory management strategies.
NPU: The On-Device AI Specialist
Key Capabilities
- Optimized for AI inference: Especially quantized INT8 or FP16 models.
- Embedded: In smartphones, AR devices, IoT endpoints.
- Tightly integrated: Run with vendor-specific ML APIs (Apple CoreML, Qualcomm Hexagon).
- Best for: Privacy-sensitive, low-latency, always-on AI, e.g., image processing, voice recognition, translation—everything that needs to happen instantly, on-device.
TPU: The Cloud AI Accelerator
Architecture & Deployment Considerations
- Massively parallel matrix processors: Systolic arrays excel at deep learning.
- Custom silicon: Google-developed for TensorFlow and XLA graph-compiled workloads.
- Cloud-first: Available through Google Cloud for both training and high-volume inference.
- Strength: High-throughput, low-level control over large model workloads.
- Limitation: Requires model and code adaptation for full performance.
Comparative Table: When to Use What?
| Processor | Best For | Not Ideal For | Common Environments |
|---|---|---|---|
| CPU | Control flow, OS logic | Massively parallel math | Laptops, servers, embedded |
| GPU | Data-parallel ML, graphics | Branch-heavy, serial code | Workstations, cloud, gaming rigs |
| NPU | On-device inference, edge AI | General compute | Smartphones, wearables, IoT |
| TPU | Cloud ML training/inference | Non-ML/general workloads | Google Cloud, datacenters |
System Design: Hybrid Architectures
- Modern SoCs: (e.g., smartphones) combine CPU, GPU, and NPU, creating multi-processor workflows where each processor handles its optimal workload.
- Cloud clusters: CPUs orchestrate jobs, while GPUs/TPUs accelerate compute-heavy ML tasks.
- Edge devices: NPUs run continuous inference, waking CPUs/GPU only when required.
Trends & Takeaways
- Hybrid compute: Seamless workflow across CPUs, GPUs, NPUs, TPUs is becoming mainstream.
- Edge AI acceleration: NPUs proliferate in consumer, industrial, and automotive spaces.
- Custom silicon: Apple Neural Engine, Google Edge TPU, and others are driving vertical integration.
- Development frameworks: TensorFlow Lite, ONNX Runtime, PyTorch Mobile abstract away hardware differences for developers.
Conclusion
- There’s no universal “best” processor; the context, workload, and product focus determine the choice.
- Use CPUs for versatility, GPUs/TPUs for large-scale ML, and NPUs for real-time edge applications.
- Hybrid approaches maximize flexibility, performance, and cost-effectiveness.
Discussion Point
Have you hit bottlenecks when moving ML workloads to GPUs (e.g., due to kernel launch overhead or memory copy limits)? What tuning approaches worked best for you—batching, paging, advanced driver settings? Share your strategies!
This article was adapted from my original blog post. Read the full version here: https://guptadeepak.com/understanding-cpus-gpus-npus-and-tpus-a-simple-guide-to-processing-units/
This content originally appeared on DEV Community and was authored by Deepak Gupta