Five AI Compute Architectures Every Engineer Should Know: CPUs, GPUs, TPUs, NPUs, and LPUs Compared

Five AI Compute Architectures Every Engineer Should Know: CPUs, GPUs, TPUs, NPUs, and LPUs Compared

Modern AI systems have transitioned from general-purpose computing to a diverse ecosystem of specialized architectures including GPUs, TPUs, and LPUs. Groq’s LPU innovation delivers up to 10x better energy efficiency for large language model inference by eliminating off-chip memory bottlenecks.

Why This Matters

Technical reality dictates that no single processor can handle the entire AI lifecycle efficiently. While CPUs are essential for system-level orchestration and complex logic, they become bottlenecks in parallel matrix operations. Engineers must navigate the trade-offs between the flexibility of GPUs and the extreme specialization of architectures like the LPU, where performance gains come at the cost of limited memory capacity per chip.

Key Insights

• CPUs act as the system ‘brain,’ managing orchestration and data flow for accelerators rather than being replaced by them.
• GPUs utilize thousands of small cores for massive parallelism, which has made them the dominant architecture for deep learning training workloads.
• Google’s TPU uses a systolic array (matrix multiply unit) to propagate data across a grid without repeated memory access, powering models like Gemini.
• NPUs enable low-power inference at the edge, often operating within single-digit watt budgets for on-device tasks like speech recognition.
• The Groq LPU utilizes a software-first, compiler-driven design to ensure deterministic execution and zero runtime scheduling overhead.

Practical Applications

• Google Cloud Platform (TPU): Optimized for serving billion-user systems like Search and Gemini via systolic data flow. Pitfall: Relying on TPUs for general-purpose logic results in inefficiency due to their lack of architectural flexibility.
• Apple Neural Engine (NPU): Integrated into SoCs to process computer vision and NLP locally on mobile devices. Pitfall: Using NPUs for large-scale training is impossible due to their focus on low-precision arithmetic and power-constrained inference.

References:

• https://www.marktechpost.com/2026/04/09/five-ai-compute-architectures-every-engineer-should-know-cpus-gpus-tpus-npus-and-lpus-compared/