CPU, GPU, TPU, NPU, LPU, DPU & QPU Explained logopeterindia.net
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CPU, GPU, TPU, NPU, LPU, DPU & QPU Explained

Seven processor types, seven different bottlenecks: what each chip is built for, how they work together in an AI stack, and how to evaluate hardware for real workloads.

38 topics•5 categories•Updated October 2026

Seven Processors, Seven Bottlenecks

3 entries

CPUs, GPUs, TPUs, NPUs, LPUs, DPUs and QPUs are not interchangeable. Each exists because a different part of computing became the bottleneck: sequential logic, parallel math, tensor operations, on-device inference, language-model latency, data movement, or problems classical chips cannot solve efficiently. Picking the right one for a workload matters more than picking the fastest on a spec sheet. In a production AI stack they divide the work. The CPU coordinates the pipeline, runs the operating system and handles input and output. GPUs and TPUs train models and run the heavy compute. An LPU can accelerate low-latency token decoding. An NPU runs inference locally on a phone, laptop or car. A DPU moves and protects data between nodes. A QPU explores problems classical hardware cannot handle efficiently. None of these chips works in isolation, so real systems combine several. The cards below summarize each type with a vendor resource; specific figures change every generation, so check the vendor pages for current specifications.

01AI Chip ProvidersDirectory of AI accelerator and chip vendors on this site. 02GPU Cloud ProvidersClouds that rent GPUs, on this site. 03MLPerf Inference benchmarksIndustry benchmark results for data center inference on different hardware.

The Seven Processor Types

7 entries

Role, strengths and limits of each, with a vendor resource.

04CPU: the coordinatorGeneral-purpose processor built around a few powerful cores, deep caches and out-of-order execution. Best for operating systems, web and database servers, control logic and orchestrating AI pipelines; limited at massively parallel math such as matrix multiplication. Recent CPUs add integrated NPUs and GPUs for edge AI, and server CPUs such as NVIDIA Grace exist to feed GPUs at high bandwidth. This card links to Intel Core Ultra; the Intel site blocks automated checks. 05GPU: the parallel powerhouseThousands of simpler cores run the same operation across large amounts of data, which suits neural network training and inference because both are dominated by matrix multiplication. Best for training and running large AI models, graphics and scientific simulation; trade-off is power use and cost compared with specialized inference silicon. Precision formats from FP32 through FP16, BF16, FP8 and lower trade accuracy for speed and memory. This card links to NVIDIA's Blackwell architecture; AMD Instinct and Intel Gaudi compete in the same space. 06TPU: the tensor specialistGoogle's custom ASIC for matrix and tensor math, built around a systolic array that avoids the constant memory traffic of general-purpose chips. Best for large-scale training and inference on Google Cloud, especially transformer workloads; trade-off is being more specialized and tied to Google's software ecosystem such as JAX, PyTorch/XLA and TensorFlow. 07NPU: the edge AI engineSmall, low-power blocks inside phone, laptop and car chipsets that run inference locally, such as photo processing, voice transcription and on-device summarization, using low-precision arithmetic at a fraction of a GPU's power. Best where latency, privacy or offline operation matter. Raw TOPS figures are hard to compare across chips with different memory architectures, because real speed also depends on memory bandwidth. This card links to Qualcomm Snapdragon. 08LPU: the low-latency specialistLanguage Processing Unit is Groq's term for its inference chip, not a universal category like CPU or GPU. It uses on-chip SRAM rather than external HBM, trading capacity for deterministic, low-latency execution that suits the token-by-token decode phase of LLM inference, but not training or vision workloads. Press reports in late 2025 described NVIDIA licensing Groq's architecture, so check Groq's site for its current position. 09DPU: the data-center offloaderA SmartNIC with its own CPU cores that offloads networking, storage virtualization and security so CPUs and GPUs stay free for compute. Best for hyperscale and enterprise data centers moving large datasets between storage, network and compute. AMD's Pensando line is in the same category. This card links to NVIDIA BlueField. 10QPU: the quantum explorerA different computing model that manipulates qubits using superposition and entanglement. It suits specific problem classes such as combinatorial optimization, cryptography and molecular simulation, not general-purpose computing. Today's systems are noisy, need cryogenic cooling and run tethered to classical control hardware, so for most enterprises QPUs remain exploratory. This card links to IBM Quantum; Google, IonQ, Rigetti and D-Wave pursue other approaches.

Vendor Resources

10 entries

Where to look up current products for each type.

11NVIDIA Grace CPUArm-based data center CPU designed to pair with GPUs. 12AMD Instinct acceleratorsAMD data center GPUs for AI and HPC. 13Intel GaudiIntel's AI accelerators; the site blocks automated checks. 14Arm CPU IPProcessor designs licensed across mobile, cloud and embedded devices. 15Cloud TPU documentationIntroduction to TPUs in Google Cloud. 16AMD Pensando DPUsAMD's data processing units. 17NVIDIA DOCASoftware framework for programming BlueField DPUs. 18IBM Quantum platformCloud access to IBM quantum systems. 19Qualcomm AI HubModels and tools for running AI on Qualcomm devices. 20Apple machine learningApple's resources for on-device machine learning.

Choosing the Right Processor

8 entries

Evaluate against the workload, not the spec sheet.

21MLPerf Inference: edgeBenchmark results for edge inference. 22MLCommonsOrganization behind the MLPerf benchmarks. 23NVLinkNVIDIA's high-bandwidth GPU interconnect, an example of interconnect that affects scaling. 24InfiniBand Trade AssociationIndustry body for the InfiniBand interconnect. 25PyTorch/XLARun PyTorch on TPUs and other XLA devices; an example of framework support as a selection criterion. 26JAXNumerical computing library for accelerators. 27ONNX RuntimeCross-platform inference engine that targets many processor types. 28LiteRTGoogle's on-device runtime for mobile and edge NPUs.

Related Pages

10 entries

Neighbouring pages on this site.

29Artificial IntelligenceAI hub. 30Quantum ComputingQuantum computing hub. 31Quantum Computing VendorsQuantum hardware and software vendors. 32Neuromorphic AIBrain-inspired computing. 33Edge AIAI at the edge. 34IT Infrastructure Hardware CompaniesHardware vendors for data centers. 35Agentic AI PlatformsAgentic AI platforms. 36Small Language ModelsSmall models for edge devices. 37Computing ParadigmsComputing hub. 38SemiconductorsSemiconductor hub.

Frequently Asked Questions

What is the difference between a CPU and a GPU?

A CPU has a few powerful cores suited to varied, sequential work such as operating systems and control logic. A GPU has thousands of simpler cores that run the same operation across large amounts of data, which suits matrix-heavy AI workloads.

What does each processor do in an AI stack?

The CPU coordinates and handles input and output; GPUs and TPUs do the heavy training and inference compute; an LPU can speed up low-latency token decoding; an NPU runs inference on the device; a DPU moves and protects data between nodes; a QPU explores problems classical hardware cannot solve efficiently.

What is an LPU?

Language Processing Unit is a term coined by Groq for its inference chip. It is vendor-specific rather than a universal processor category, and it targets low-latency LLM decoding rather than training.

Why are TOPS figures misleading?

TOPS measures peak arithmetic throughput, but real inference speed also depends on memory bandwidth, precision, software and the model. Chips with different memory architectures cannot be fairly compared on TOPS alone.

What should I evaluate when choosing hardware?

Latency, throughput, memory capacity and bandwidth, numerical precision, power efficiency, framework and tooling support, interconnect, scalability and total cost for your actual workload. Use benchmarks such as MLPerf and test your own models.

Are QPUs ready for production?

Generally not. Quantum hardware is still noisy and error-prone, so most organizations use it for research and experimentation rather than production workloads.

How current is this page?

Links were checked in October 2026. Chip specifications, product names and ownership change quickly, so confirm figures on each vendor's own page. Sites that block automated checks are kept and may need a manual click.