GPU vs TPU vs LPU: differences and use cases
- Cedric KTORZA
- Oct 15
- 6 min read
GPU/TPU/LPU: understand the differences and use cases. This no‑nonsense guide clarifies what each accelerator does best, when to choose one over the others, and how to align your AI stack with performance, latency, and sustainability goals.
In brief
GPUs are general‑purpose accelerators with the richest ecosystem—great for training and versatile inference.
TPUs specialize in tensor math for deep learning—excellent for large‑scale training and batched inference on Google Cloud.
LPUs target ultra‑low‑latency, high‑throughput LLM inference—designed to serve tokens fast at scale.
Choosing depends on model size, latency/throughput, budget, deployment (cloud/on‑prem/edge), and energy constraints.
At Score Group, we align compute choices with your infrastructure and energy strategy—where efficiency meets innovation.
What each accelerator really is
GPUs: general‑purpose parallel compute
Graphics Processing Units are massively parallel processors originally built for rendering, now the de‑facto workhorse for AI. They offer:
Mature software stacks (CUDA, cuDNN, PyTorch, TensorFlow) and vast community support
Strong performance for both training and inference across CV, NLP, recommendation, and multimodal workloads
Broad availability on all major clouds and on‑prem data centers
Learn more: NVIDIA’s CUDA ecosystem overview explains the programming model and libraries developer.nvidia.com/cuda-zone. For frameworks, see PyTorch pytorch.org.
TPUs: tensor processors tuned for deep learning
Google’s Tensor Processing Units are ASICs optimized for matrix/tensor operations common in deep networks. Key traits:
Highly efficient systolic arrays and interconnects for training and inference at scale
Tight integration with JAX and TensorFlow; optimized input pipelines and XLA compilation
Offered as managed hardware on Google Cloud (e.g., v5e/v5p as of 2024–2025)
Explore: TPU v5e documentation on Google Cloud cloud.google.com/tpu/docs/v5e, JAX docs jax.readthedocs.io, and Google’s early TPU paper (architecture background) arxiv.org/abs/1704.04760.
LPUs: low‑latency engines for LLM inference
LPUs (notably from Groq) are accelerators designed for deterministic, ultra‑low‑latency inference, particularly for large language models. Highlights:
Architected to minimize token latency and maximize consistent throughput for generation
Strong fit for chatbots, retrieval‑augmented generation, and streaming workloads where responsiveness matters
Typically focused on inference today (as of 2025), not training
Start here: Groq LPU overview groq.com/lpu. For broader context on efficient LLM inference techniques, see surveys on serving and optimization arxiv.org/abs/2309.06180.
“Where efficiency meets innovation.” At Score Group, we bridge Energy, Digital, and New Tech to turn AI ambition into performant, sustainable reality.
Core differences that matter in practice
Compute model and performance envelopes
GPUs: Flexible SIMT/SIMD cores with tensor units; excel across training and inference, from vision to multimodal. Best all‑rounder when you need versatility.
TPUs: Systolic arrays + fast interconnects; shine on large, batched matrix multiplies and scale‑out training on Google Cloud.
LPUs: Purpose‑built for predictable, low‑latency token generation; ideal for LLM serving at scale with strict SLA.
Performance evolves fast; independent benchmarks (e.g., MLPerf by MLCommons) publish periodic results across tasks and hardware without vendor bias mlcommons.org.
Memory hierarchy and model fit
GPUs: Large HBM stacks; strong support for tensor/parameter sharding, quantization, and KV‑cache optimizations.
TPUs: High on‑device bandwidth with TPU interconnect; tight compiler integration can improve memory reuse.
LPUs: Architected for steady token pipelines and KV‑cache efficiency; focus on inference memory patterns.
Choosing the right precision (FP8/BF16/INT8/quantization) and cache strategy can shift the balance. Libraries like Hugging Face Optimum help optimize across hardware huggingface.co/docs/optimum.
Programming model and ecosystem
GPUs: Deep ecosystem (CUDA, cuDNN, Triton, PyTorch, TensorFlow), mature tooling, broad model zoo support.
TPUs: Best with JAX/TensorFlow and XLA; great for teams leaning into Google’s ML stack.
LPUs: SDKs and runtimes focused on inference; increasingly friendly to common LLM toolchains.
Deployment and operations
GPUs: Available everywhere—edge to hyperscalers; ideal for hybrid and on‑premises strategies.
TPUs: Cloud‑native on Google Cloud; managed scale, strong training/inference economics at scale.
LPUs: Cloud or dedicated inference services; designed for production LLM serving with low latency.
Operational considerations include observability, autoscaling, CI/CD for models, and energy footprint—areas where aligning IT and energy strategies is crucial.
GPU vs TPU vs LPU at a glance
Typical use cases and what to pick
Training large models and fine‑tuning
Pick GPUs when you need flexibility across frameworks, mixed precision, custom kernels, and varied model types.
Choose TPUs if you’re committed to JAX/TensorFlow and plan large‑scale training on Google Cloud (shard across pods).
LPUs are currently geared toward inference; use GPUs/TPUs for training, then deploy to LPUs if low‑latency serving is critical.
Reference stacks: PyTorch for training on GPUs pytorch.org; TPU training with JAX jax.readthedocs.io.
Real‑time inference for chat, agents, and RAG
LPUs are strong for interactive LLMs with strict time‑to‑first‑token and steady tokens/sec SLAs.
GPUs excel when you need heterogeneous models (vision+LLM, embeddings, TTS) in a single cluster.
TPUs fit batched inference pipelines on Google Cloud, especially at scale with predictable traffic.
Explore serving and optimization techniques for LLMs arxiv.org/abs/2309.06180.
Analytics, embeddings, and vector search
GPUs: Great for high‑throughput embeddings, ANN search, and hybrid pipelines (ETL + inference).
TPUs: Use for TensorFlow/JAX‑centric pipelines integrated with Google Cloud data services.
LPUs: Deploy to serve LLM responses while embeddings/search run elsewhere; interconnect via microservices.
Simulation, digital twins, and HPC
GPUs remain the primary choice thanks to mature HPC toolchains and mixed workloads (numerical + ML).
TPUs focus on DL workloads; use when simulation is ML‑driven or you operate primarily in Google Cloud.
LPUs are not aimed at physics/HPC—reserve them for inference tiers.
Independent results evolve; check MLPerf for the latest trends mlcommons.org.
Edge and sovereignty constraints
GPUs offer the most options for edge appliances and on‑premises clusters.
TPUs are cloud‑centric; consider only if your compliance allows managed services.
LPUs may be offered as services; verify deployment models and data governance guarantees.
A pragmatic selection framework
Define workload: training, batch inference, or interactive inference? Model family and size?
Prioritize latency vs throughput: Do you need sub‑100 ms responses or maximum cost‑efficient tokens/images per second?
Validate ecosystem fit: PyTorch/TensorFlow/JAX? Required kernels and ops?
Plan deployment: Edge, on‑prem, private cloud, public cloud, or hybrid?
Optimize efficiency: Precision (BF16/FP8/INT8), quantization, KV‑cache, and batching strategies.
Stress‑test with real data: Run POCs with production‑like payloads; use vendor‑agnostic benchmarks and MLPerf as a guide mlcommons.org.
Align with sustainability goals: Measure power, PUE, and tokens/images per watt; integrate telemetry into energy KPIs.
Tools to help: CUDA/TensorRT (GPUs) developer.nvidia.com/cuda-zone, XLA/JAX (TPUs) jax.readthedocs.io, and inference optimization frameworks like Optimum huggingface.co/docs/optimum.
How Score Group connects Energy, Digital and New Tech
At Score Group, we integrate AI acceleration choices into a cohesive plan that serves performance, security, and sustainability—“where efficiency meets innovation.”
Noor ITS (Digital): Architecture for networks, systems, data centers, cloud/hybrid hosting, cybersecurity, and resilience. We size clusters, design interconnects, and prepare PRA/PCA for AI services.
Noor Technology (New Tech): AI platforms, RPA, IoT, and custom applications. We operationalize model training/inference, from MLOps to real‑time serving with GPUs, TPUs, or LPUs.
Noor Energy (Energy): Smart energy management for data centers and sites—monitoring, GTB/GTC, renewable integration, and storage—to maximize efficiency and reduce the carbon footprint of AI workloads.
Discover our integrated approach: Score Group – Des solutions adaptées à chacun de vos besoins.
FAQ
Are LPUs replacing GPUs for AI?
Not broadly. As of 2025, LPUs are specialized for ultra‑low‑latency LLM inference, while GPUs remain the most versatile accelerator for both training and diverse inference tasks (vision, audio, multimodal, recommendation). Many production architectures combine them: train and batch‑embed on GPUs/TPUs, then serve interactive LLM traffic on LPUs for predictable low latency. Hardware ecosystems evolve quickly, so keep an eye on vendor roadmaps and independent benchmarks such as MLPerf mlcommons.org.
Do I need TPUs to train transformer models at scale?
No, but they can be advantageous. TPUs provide efficient scale‑out training—particularly with JAX/TensorFlow and XLA—on Google Cloud. GPUs, however, dominate the training ecosystem with PyTorch and wide library support, and are available across all clouds and on‑prem. Your choice should reflect framework preference, data locality, compliance, and how you plan to deploy inference. For TPU specifics, see Google Cloud documentation cloud.google.com/tpu/docs/v5e.
How does latency differ between GPU, TPU, and LPU for LLMs?
Latency is workload‑dependent. LPUs are engineered for deterministic, low time‑to‑first‑token and steady tokens/sec, making them strong for conversational agents. GPUs can reach excellent latency with optimized kernels, quantization, and batching—especially for multimodal pipelines. TPUs perform well in batched scenarios and at cloud scale. Always benchmark your target model, sequence lengths, and batching strategy; optimization guides like Hugging Face Optimum help standardize tests huggingface.co/docs/optimum.
Which accelerator is most energy‑efficient?
It depends on utilization, precision, and the match between hardware and workload. For training at scale, TPUs and high‑end GPUs can both be efficient when well‑utilized and properly cooled. For LLM serving, LPUs can deliver strong tokens/sec per watt due to their specialization. Beyond silicon, facility metrics (PUE), renewable integration, and load management materially impact sustainability—areas where aligning IT and energy strategies is essential.
Key takeaways
GPUs: your safest all‑round choice for training and varied inference with a rich ecosystem.
TPUs: compelling for large‑scale training/inference on Google Cloud with JAX/TensorFlow.
LPUs: purpose‑built for ultra‑low‑latency LLM serving and deterministic throughput.
Optimize before you scale: precision, quantization, batching, and KV‑cache strategies drive outcomes.
Blend compute with energy strategy: measure tokens/images per watt and improve PUE.
Ready to align AI performance with sustainability and resilience? Let’s make it happen together: Score Group.
