GPU vs LPU vs TPU: Choosing the Right AI Accelerator

As AI systems move from experiments to 24/7 production, one question comes up in almost every project:

“Which accelerator should we use — GPU, LPU, or TPU?”

There is no single best chip. The right choice depends on what kind of AI work you run, how fast decisions must be made, and how the system is integrated.

This article explains the differences without marketing hype, from a system-architecture perspective.

1. GPU (Graphics Processing Unit)

What it was designed for

Originally for graphics → evolved into a general-purpose parallel compute engine.

Strengths

Excellent for AI training
Strong ecosystem (PyTorch, TensorFlow)
Flexible: vision, LLMs, audio, simulation
Easy to prototype and scale

Weaknesses

High power consumption
Overkill for simple inference
Costly at scale for always-on workloads

Best use cases

Model training
Research & experimentation
Multi-purpose AI workloads
Computer vision pipelines

Think of GPU as:
A powerful factory with many machines — flexible, but expensive to keep running.

2. LPU (Language Processing Unit)

What it was designed for

Ultra-fast inference, especially for language models.

Strengths

Extremely low latency
Deterministic execution (predictable timing)
Excellent for real-time AI
Very high token-per-second throughput

Weaknesses

Limited flexibility
Not suitable for training
Smaller ecosystem than GPUs
Best when workload is well-defined

Best use cases

Chatbots with real-time response
AI assistants
Edge or near-edge inference
High-QPS inference servers

Think of LPU as:
A race car — unbeatable on a track, useless off-road.

3. TPU (Tensor Processing Unit)

What it was designed for

An AI-specific accelerator, optimized for tensor operations.

Strengths

Very efficient for large-scale training
Cost-effective at massive scale
Excellent for batch ML workloads

Weaknesses

Cloud-only in most cases
Limited customization
Vendor lock-in concerns

Best use cases

Cloud-native ML
Large batch training
Ecosystems tightly coupled to specific cloud providers

Think of TPU as:
A specialized industrial plant — efficient, but only inside one ecosystem.

4. Quick Comparison Table

Feature	GPU	LPU	TPU
Training	⭐⭐⭐⭐⭐	❌	⭐⭐⭐⭐
Inference latency	⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐
Flexibility	⭐⭐⭐⭐⭐	⭐⭐	⭐⭐⭐
Power efficiency	⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐
Ecosystem	⭐⭐⭐⭐⭐	⭐⭐	⭐⭐⭐
Best for	General AI	Real-time AI	Cloud ML

5. How to Choose (System-First Thinking)

Decision flow diagram (Mermaid)

flowchart TD
    A["Start: Define your AI workload"] --> B["Are you TRAINING models?"]

    B -->|"Yes"| C["Do you need cloud-native scale and you’re OK with a managed ecosystem?"]
    C -->|"Yes"| T1["Choose TPU
(best for large-scale training & batch ML in managed cloud)"]
    C -->|"No"| G1["Choose GPU
(best for flexible training, prototyping, and mixed workloads)"]

    B -->|"No (Inference)"| D["Is LOW LATENCY (real-time response) a hard requirement?"]
    D -->|"Yes"| E["Is the workload mostly LLM / text generation
with stable, well-defined deployment?"]
    E -->|"Yes"| L1["Choose LPU
(best for ultra-low-latency, high-throughput inference)"]
    E -->|"No"| G2["Choose GPU
(best for real-time inference across diverse models)"]

    D -->|"No"| F["Is this batch/async inference or multi-model serving?"]
    F -->|"Yes"| G3["Choose GPU
(best overall flexibility and ecosystem)"]
    F -->|"No"| H["Are you locked into a specific cloud ML stack?"]
    H -->|"Yes"| T2["Choose TPU
(cost-effective at massive scale in cloud)"]
    H -->|"No"| G4["Choose GPU (default safe choice)"]

    %% Integration reminder
    L1 --> Z["Validate integration: latency budget, data flow, fallback, observability"]
    G1 --> Z
    G2 --> Z
    G3 --> Z
    G4 --> Z
    T1 --> Z
    T2 --> Z

Instead of asking “Which chip is fastest?”, ask these questions:

Is this training or inference?

Training → GPU or TPU
Inference → LPU or GPU

Is latency critical?

Sub-second decisions → LPU
Batch or async workloads → GPU or TPU

Is this edge, on-prem, or cloud?

Edge / on-prem → GPU or LPU
Cloud-native → TPU

Will the model change often?

Yes → GPU
Rarely → LPU

6. A Common Architecture Pattern

[ Sensors / Users ]
        ↓
[ GPU Training Cluster ]
        ↓
[ Model Export ]
        ↓
[ LPU Inference Engine ]
        ↓
[ Business Logic / ERP / MES ]

This hybrid approach: