What is the LPU inference engine and how does it work?

Over the past several months I have been spending more and more time working with AI so this post is going to be dramatically different than my previous posts, at least the topic is going to be.

The pace of change in AI is hard to keep up with, it reminds me of the early years of Azure when my clients would always at some point ask me ‘How do you keep up with all the changes in Azure?’  I think AI change is even more rapid but maybe it’s just because it’s happening now, and Azure is a fairly stable platform regarding changes now. 

Let me introduce you to Groc LPU. Most of my AI work is done in the cloud, partly because my laptop doesn’t have an NVIDIA card but also because I need to use it for other things and so I will use Azure VM’s for LLM work and Azure for AI, which comes with a cost. I have done some transcribing using Whisper of lectures that I have on DVD and that has been running for weeks now. Transcribing video to text is ideally done with an NVIDIA card and CUDA, which is expensive in the cloud. Since I have a $150/mo credit through MSDN, I use a D series VM and it just takes longer, Microsoft won’t even allow me to have a VM with an NVIDIA GPU on my MSDN subscription, only on my pay-as-you-go, hence the D series. So, when I saw Groq a couple weeks ago I was amazed at the speed >300 tokens per second and no GPU?! Amazing. 


So, what is Groq and what makes them different?

The Groq LPU (Language Processing Unit) Inference Engine represents a groundbreaking approach in the field of artificial intelligence, specifically tailored for the efficient processing of language models. This essay aims to demystify the LPU inference engine, exploring its operational principles, architectural design, and the implications of its introduction into the AI landscape.

How the LPU Inference Engine Works

At its core, the LPU Inference Engine is designed to address the two primary bottlenecks encountered by large language models (LLMs): compute density and memory bandwidth[2]. Unlike traditional GPUs, which are hampered by external memory bandwidth bottlenecks, the LPU boasts a design that allows for faster generation of text sequences by significantly reducing the time per word calculated. This is achieved through a combination of exceptional sequential performance, a single-core architecture, and synchronous networking that is maintained even in large-scale deployments[2].

The LPU’s architecture is purpose-built for inference, offering a simple yet efficient design that prioritizes inference performance and precision. This is particularly evident in its ability to auto-compile models larger than 50 billion parameters, provide instant memory access, and maintain high accuracy even at lower precision levels[2]. Such capabilities are a testament to the LPU’s innovative approach to tackling the challenges posed by LLMs.

Architectural Design

The LPU’s specialized architecture is a departure from conventional inference methods, with a focus on the sequential processing patterns inherent in language. This contrasts with GPUs, which are optimized for parallel computations suited for graphics processing[3]. The LPU’s design minimizes inefficiencies and maximizes throughput for LLM inference tasks by:

  • Tailoring for Sequential Processing: Unlike GPUs, the LPU prioritizes the sequential nature of language, ensuring efficient handling of LLM workloads[3].
  • Enhanced Compute Density: The LPU packs more processing power into a smaller footprint compared to GPUs, enabling faster execution of LLM tasks[3].
  • Optimized Memory Bandwidth: High-bandwidth memory equips the LPU with rapid access to the information required for LLM inference, further accelerating processing speeds[3].

Implications and Benefits

The introduction of the Groq LPU Inference Engine into the AI landscape heralds a new era of efficiency and performance in LLM processing. Its ability to deliver unprecedented inference speeds significantly outperforms traditional GPU-based approaches, unlocking a multitude of advantages for developers and users alike[3]. These benefits include reduced operational costs due to lower power consumption, enhanced scalability to accommodate the growing demands of complex LLMs, and the democratization of LLM capabilities, making this technology more accessible to a wider range of developers and businesses[3].

Moreover, the LPU’s software-first designed hardware solution simplifies the developer experience by eliminating the need for schedulers, CUDA libraries, kernels, and more. This streamlined approach ensures that data travels through the LPU with minimal friction, akin to every traffic light turning green precisely when needed[4].


The Groq LPU Inference Engine marks a significant milestone in the evolution of AI processing technology. By addressing the critical challenges of compute density and memory bandwidth, the LPU offers a tailored solution that enhances the efficiency and performance of LLM inference tasks. Its specialized architecture and innovative design principles not only set a new standard for inference speed but also pave the way for broader adoption and application of AI technologies in real-world scenarios. As the AI landscape continues to evolve, the Groq LPU stands out as a pivotal development that promises to reshape our approach to AI processing and utilization.

[1] https://www.reddit.com/r/ArtificialInteligence/comments/1ao2akp/can_anyone_explain_me_about_groq_lpu_inference/
[2] https://wow.groq.com/lpu-inference-engine/
[3] https://promptengineering.org/groqs-lpu-advancing-llm-inference-efficiency/
[4] https://cointelegraph.com/news/groq-breakthrough-answer-chatgpt
[5] https://wow.groq.com/why-groq/
[6] https://sc23.supercomputing.org/proceedings/exhibitor_forum/exhibitor_forum_files/exforum135s2-file2.pdf
[7] https://youtube.com/watch?v=N8c7nr9bR28
[8] https://newatlas.com/technology/groq-lpu-inference-engine-benchmarks/
[9] https://www.linkedin.com/pulse/why-groqs-lpu-threat-nvidia-zack-tickman-2etyc?trk=article-ssr-frontend-pulse_more-articles_related-content-card
[10] https://www.prnewswire.com/news-releases/groq-lpu-inference-engine-leads-in-first-independent-llm-benchmark-302060263.html
[11] https://siliconangle.com/2023/11/17/inside-groqs-lpu-impact-generative-ai-sc23/
[12] https://youtube.com/watch?v=WQDMKTEgQnY
[13] https://www.kavout.com/blog/groq-lpu-chip-a-game-changer-in-the-high-performance-ai-chip-market-challenging-nvda-amd-intel/