LPU vs GPU: What’s the Difference? 

If you’re interested in artificial intelligence, natural language processing, or large language models (LLMs), you’ve probably heard about the fierce competition between different models and training routines. There’s an ever-growing number of models that try to innovate how their AI learns or what it’s capable of. But the hardware backbones that support those models are also seeing rapid change and development—including a landmark industry shift announced in December 2025.

Language Processing Units (LPUs) are chips developed to aid in the development and deployment of LLMs. Built from the ground-up to make language inference fast and efficient, LPUs have clearly proven their value—NVIDIA entered into a $20 billion non-exclusive licensing agreement with Groq in late December 2025, acquiring the intellectual property, engineering team, and hardware stack behind LPU technology. 

Below, we explain what LPUs are, how they’re different from GPUs, recent industry developments, and what they’re capable of.​

What is an LPU?

LPU stands for Language Processing Unit. LPU is a proprietary chip architecture developed by Groq, a company founded in 2016 by Jonathan Ross, the former lead engineer behind Google’s Tensor Processing Unit (TPU). LPUs are a crucial part of LPU Inference Engines, which are a new type of end-to-end processing unit system for the applications and workloads that are most commonly associated with natural language processing or AI language applications.​

Architecturally, LPUs are designed for sequential, rather than parallel, computationally intensive applications. Groq developed LPUs to be inherently efficient and powerful at handling LLMs, or Large Language Models. The LPU can be deployed in any architecture and can support nearly any training model. With appropriate storage solutions, an LPU can process huge volumes of data and efficiently handle computational demands of inference tasks at speeds that have reshaped industry expectations.

Groq’s LPU is a chip and processing system that has exceptionally fast sequential performance, relies on a single massive core architecture with hundreds of megabytes of on-die SRAM, provides synchronous networking for deployments at scale, can purportedly auto-compile Large Language Models larger than 50 billion parameters, and has near-instant memory access with cycle-level determinism. Unlike GPUs that rely on external high-bandwidth memory (HBM3), the LPU’s static execution model means the compiler pre-plans the entire program path and guarantees predictable latency with no cache misses or stalls.​

These capabilities arise from LPUs being custom-built to facilitate a wide range of natural language processing applications, including text generation, sentiment analysis, language translation and more. 

Independent benchmarks conducted by ArtificialAnalysis.ai in early 2024 confirmed that Groq’s LPU achieved throughput of 241 tokens per second for Llama 2 Chat (70B), more than double the speed of other hosting providers. By 2025, Groq demonstrated even higher performance on newer models, with tests showing 877 tokens per second for Llama 3 8B and 284 tokens per second for Llama 3 70B.​

What is a GPU?

A GPU, or graphics processing unit, is a more recognizable piece of hardware, a specialized component engineered to handle demanding graphics-rendering tasks. Although they were originally designed for processing imagery output to a display device, their capacity for computationally intensive operations has led to many more applications, extending their use to artificial intelligence and scientific computing.

GPUs excel in parallel workloads, providing the results of thousands of small computations nearly instantaneously. This parallel workload strength has made GPUs indispensable in tasks that require data parallelism like image processing, simulations and machine learning.

Architecturally speaking, high-end GPUs can have thousands of cores, from which they get their strength at processing computations across a vast number of processing elements in tandem. This has made them increasingly valuable in AI, where they’ve been used to train and deploy deep learning models. Parallel processing accelerates complex neural network training.

GPUs are also highly versatile, adapting to various architectures and supporting a wide spectrum of training models. Their parallel architecture combined with their high-speed memory and optimized data throughput, and advanced memory management techniques means GPUs can process large volumes of data when storage solutions are optimally configured. NVIDIA’s upcoming Rubin series and current Blackwell GPUs rely on high-bandwidth external memory (GDDR7 or HBM3) and highly parallel core layouts that scale efficiently for training and large-batch inference.​

Differences between LPU and GPU

The key difference between GPUs and Groq’s LPUs is parallel vs. sequential processing and their memory architecture. GPUs excel at breaking up complex tasks into thousands of tiny computations to be performed simultaneously, utilizing external high-bandwidth memory. But much of AI inference work is sequential in nature, so parallel processing isn’t a particular advantage. An LPU is designed specifically for the tasks that make it possible for AI to understand and generate human language in real-time, leveraging on-chip SRAM to eliminate external memory latency.​

When it comes to tasks related to training and using LLMs, things like translation, chatbots, and content generation, the biggest place an LPU outperforms a GPU is in terms of inference speed and power efficiency for single-stream workloads. Where GPUs perform best with large batch sizes, LPUs optimize for single-token inference—the exact characteristic needed for real-time AI applications like chatbot hosting and real-time agents. Processing time and energy use are both notably lower when using an LPU for sequential natural language processing tasks, with benchmarks showing LPUs can deliver approximately 2-3x speedups on key AI inference workloads compared to GPU-based solutions.​

That efficiency and speed can lead to bottlenecks with traditional data storage environments. An LPU requires a storage solution that keeps pace with its rapid processing capabilities in order to maintain high levels of performance. Latency issues and diminished overall efficiency is possible if data can’t be delivered to the LPU quickly enough, or categorized and stored away afterward. 

Through-put, shared and scaled-out data storage architectures like Everpure FlashBlade//S™ are fast enough for modern LPU-enabled AI inference engines.

The NVIDIA-Groq Deal: What It Means for AI Infrastructure

In December 2025, NVIDIA announced a landmark $20 billion non-exclusive licensing agreement with Groq, marking NVIDIA’s largest AI-related transaction since its Mellanox acquisition. The deal grants NVIDIA access to Groq’s entire hardware stack, including the LPU IP, compiler toolchain, and silicon design. This strategic move signals NVIDIA’s recognition that ultra-low-latency inference represents a critical complement to its GPU-dominated training business.​

The agreement is structured to allow NVIDIA to integrate Groq’s deterministic, SRAM-based architecture into its product roadmap while avoiding a full regulatory merger review. Industry analysts expect NVIDIA to release software updates to its TensorRT and CUDA libraries that incorporate LPU-derived optimization techniques, with the 2026 “Vera Rubin” platform anticipated to feature a hybrid GPU-LPU design. This would enable single racks of NVIDIA hardware to handle both massive parallel training and ultra-fast sequential inference seamlessly.​

Meanwhile, GroqCloud continues to operate as an independent service, having grown from approximately 356,000 developers to over 2 million by late 2025. Prior to the NVIDIA deal, Groq had raised $1.8 billion in funding and secured a $1.5 billion commitment from Saudi Arabia for expanded LPU-based AI inference infrastructure deployment.​

Use Cases for LPUs

The ideal use case for an LPU is language-related AI inference tasks, particularly those requiring real-time response. While GPUs remain essential for training, LPUs excel at inference tasks where trained models are being applied to new data in production environments. That means LPUs are a strong choice for models that facilitate chatbots, apps that need content generated dynamically, machine translation, localization, and real-time AI agents—applications where low latency and consistent performance are critical.​

The LPU’s deterministic architecture and on-chip SRAM make it particularly well-suited for single-sequence workloads where predictable, repeatable performance matters more than aggregate throughput. This is why cloud vendors and startups racing to scale real-time generative AI services have shown strong interest in Groq’s technology.​

On-prem, full-stack flash storage such as the AI-Ready Infrastructure (AIRI®) from Everpure would be capable of the high-speed data access and throughput required to support a deployed LPU running NLP tasks, ensuring efficient retrieval and processing of vast amounts of linguistic data without creating storage bottlenecks.

Use Cases for GPUs

GPUs remain the obvious choice for rendering graphics, whether in gaming or video editing or other multimedia applications. A GPU’s inherent skill is in simulating physics and rendering thousands of graphics-related computations simultaneously, and that dominance is unlikely to change any time soon.

Even with the rise of LPUs and NVIDIA’s strategic integration of both technologies, GPUs will remain extremely powerful tools in AI processing. Parallel processing is still essential when training deep neural networks and handling huge datasets. GPUs continue to be the gold standard for large-batch inference and training workloads where throughput at scale matters most. GPUs are also versatile processing systems, making them valuable parts of holistic AI application processing environments—particularly as hybrid architectures emerge that pair GPUs for training with LPUs for inference.​

Related reading: TPU vs. GPU: What’s the difference?

Conclusion

The AI hardware landscape has evolved dramatically since LPUs first emerged. With a data storage environment capable of providing data at sufficient speeds, an LPU can process natural language inference faster and more efficiently than other hardware thanks to its purpose-built architecture that accelerates sequential processing and boasts near-instant memory access through on-chip SRAM.

Where GPUs excel at parallel processing and division of labor, Groq’s LPUs are specially designed for deterministic, low-latency sequential processing. The December 2025 NVIDIA-Groq agreement validates the LPU’s architectural thesis and signals a future where AI infrastructure increasingly relies on hybrid systems—pairing GPUs for training and large-batch workloads with LPUs for real-time inference applications. Their custom nature does make LPUs highly specialized processing systems that won’t always be the ideal choice for general-purpose computations or processing tasks. But when it comes to fast, efficient, and predictable processing of language inference workloads, LPU technology has proven itself as a transformative innovation in the AI acceleration space.​

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