The 6 Processors Powering Modern AI: CPU GPU TPU NPU LPU and DPU Explained

Every AI workload runs on hardware. Most engineers understand one or two processors in the stack. The ones building efficient AI systems understand all six — because choosing wrong costs orders of magnitude in performance, power, and money.

Here's the complete breakdown.

CPU — The Backbone (General Purpose | Sequential | Orchestration)

The CPU does everything and is optimized for none of the things AI workloads need most.

What it does in AI:

• User request arrives → CPU schedules processing
• Routes to the correct processor for the actual computation
• Manages I/O
• Returns the result

Strengths: Handles any task, best single-thread performance, runs the OS and all orchestration logic.

Weaknesses: Slow at parallel math (the core of neural network inference), cannot train large models efficiently, low throughput for AI workloads.

When to use it: Orchestration, preprocessing, routing, anything that requires flexible sequential logic.

GPU — The Workhorse (Parallel | CUDA Cores | Training)

The GPU does one thing extraordinarily well: massive parallel matrix multiplication. Since neural network training and inference are fundamentally matrix operations, GPUs became the default AI accelerator.

What it does in AI:

• Load model weights into VRAM
• Distribute computation across 16,896 parallel CUDA cores
• Matrix multiply (the bulk of transformer computation)
• Backpropagate gradients (for training)

Strengths: Massive parallelism, handles both training and inference, huge CUDA ecosystem.

Weaknesses: 700W+ power draw per H100, $30K+ per H100, complete overkill for small inference tasks.

When to use it: Training large models, high-throughput inference, fine-tuning.

NVIDIA dominates this space. One company's architectural decisions shape the entire industry.

TPU — Google's Weapon (Systolic Array | Google | Tensor Math)

TPUs are Google's custom silicon, designed from the ground up for tensor operations. The systolic array architecture processes data flows in lockstep — every computation is deterministic, no wasted cycles.

What it does in AI:

• Load model weights
• Systolic array processes data in lockstep
• Matrix operations execute on-chip
• Pod scales to 9,216 TPUs

Strengths: 2x cheaper than GPU, 2–3x better performance per watt, scales to 9,216 TPUs in a pod for massive training runs.

Weaknesses: Google Cloud only, less flexible than GPUs, limited framework support (optimized for JAX/TensorFlow, less so for PyTorch).

When to use it: Google-scale tensor workloads, if you're already on GCP, training runs that benefit from massive parallelism.

NPU — AI in Your Pocket (Edge AI | On-Device | Low Power)

Neural Processing Units are the AI accelerators built into mobile chips — the engine behind on-device AI inference in your phone. Apple's Neural Engine, Qualcomm's Hexagon, and similar chips are NPUs.

What it does in AI:

• User input arrives
• On-device NPU activates — no cloud call
• INT8/INT4 quantized inference (smaller, faster models)
• Inference in milliseconds

Strengths: Single-digit watt power consumption, zero cloud latency (~5ms), data stays on device (privacy).

Weaknesses: Inference only — can't train, limited model size (quantized models fit on device), less accurate than GPU-based cloud inference.

When to use it: Mobile AI features, privacy-sensitive inference, any use case where cloud latency is unacceptable.

LPU — The Fastest Alive (Groq | SRAM | Deterministic Inference)

Groq's Language Processing Unit takes a different approach: instead of processing tokens one at a time from slow HBM memory (like GPUs), the LPU stores all model weights on-chip in SRAM, eliminating memory access latency entirely.

What it does in AI:

• Prompt arrives
• Weights already loaded from on-chip SRAM (230MB per chip, zero cache misses)
• Deterministic execution
• 241 tokens/second — 500 words in ~1 second

Strengths: Fastest inference available (241 t/s), zero cache misses, completely deterministic execution (no variance in response time).

Weaknesses: Inference only, limited memory per chip (230MB means small-to-medium models only), needs hundreds of chips linked for large models.

When to use it: Real-time LLM serving, applications where latency is the primary constraint, high-volume inference where cost per token matters.

DPU — The Invisible Layer (SmartNIC | Offload | Infrastructure)

The DPU (Data Processing Unit) is the processor you've never heard of that runs in every modern data center. It intercepts network traffic and infrastructure work at the hardware level, offloading it from the CPU.

What it does in AI:

• Network traffic arrives
• DPU intercepts at hardware level
• Encryption + firewall runs on DPU
• Storage I/O and routing handled on DPU
• CPU freed entirely for AI workloads

Strengths: Frees CPU for application and AI work, hardware-level network security, 400Gb/s networking.

Weaknesses: Not relevant for edge or consumer applications, complex to configure, niche use case.

When to use it: Data center infrastructure, anywhere CPUs are being taxed by networking and storage I/O rather than compute.

The Decision Framework

Choose based on: latency requirements, parallelism needs, power budget, cost, and scale. There's no universal winner — only the right processor for the right job.

Jason Newell is an AI practitioner, builder, and writer covering agentic systems, developer tooling, and the future of AI engineering.