Custom AI Silicon - Emerging Challengers & Innovations: Part 1- Groq

AI is compute intensive and NVIDIA’s GPUs have long dominated this space. Here we look at how innovators like Groq are pushing the boundaries.

Feb 15, 2025

Artificial intelligence (AI) has started to revolutionize the way we work, and this interest has resulted in huge investments in the space. One crucial area of interest and investment is in the companies developing specialized chips for AI. While NVIDIA has long held the undisputed leadership in this area, other companies like Groq, Cerebras, Amazon, and Google are now manufacturing their own chips with unique characteristics. In this deep dive we will focus on these companies and examine their innovations and their impact.

Groq:

Groq, Inc. is an American artificial intelligence company that builds an AI accelerator application-specific integrated circuit (ASIC). Their ASIC called the Language Processing Unit (LPU) and related hardware is used to accelerate the inference performance of AI workloads. Here is a comparison of how fast they are compared to others.

Architectural Paradigm Shift

Groq’s flagship innovation lies in its radical simplification of processor design. By eliminating traditional architectural complexities—such as caching, speculative execution, and core-to-core communication—Groq achieves unprecedented compute density. The company’s software-defined hardware model transfers control from the chip to the compiler, enabling deterministic execution planning during compilation. This approach eliminates the need for runtime optimizations, allowing developers to predict memory usage and latency upfront, thereby accelerating deployment cycles.

The Language Processing Unit (LPU), Groq’s specialized AI accelerator, is a great example of this philosophy. LPU is optimized for large language model (LLM) inference. An LPU is made up of multiple TSPs (Tensor Streaming Processors). The LPU streamlines data flow by automating hardware resource allocation, reducing reliance on external networking components. This design not only cuts power consumption by 10x compared to GPUs but also simplifies programming, enabling "push-button" deployment of AI models.

Unlike general-purpose GPUs, Groq’s focus on LLMs allows it to strip away redundant circuitry, achieving 1.8x higher performance per watt in real-world benchmarks.

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TSP vs GPU

Groq's Tensor Streaming Processor (TSP) differs from traditional GPU architectures in several key ways:
Simplified Design: The TSP uses a single-core approach, unlike the multi-core design of traditional GPUs. This simplicity allows for more efficient use of chip space and power.
Deterministic Execution: TSP architecture ensures predictable and deterministic performance compared to a GPU.
Memory Management: TSP uses a centralized block of SRAM, which is more efficient than the fractured memory design of traditional GPUs.
Compiler-driven Optimization: In a TSP, the compiler handles instruction and data flow scheduling, eliminating the need for complex hardware-level optimizations.
Scalability: TSPs can be linked together without the typical bottlenecks seen in GPU clusters, allowing for linear performance scaling.
Energy Efficiency: The TSP design provides better performance per watt compared to traditional GPU accelerators.
Software-defined Approach: Unlike GPUs that rely on hardware components for flow control, TSP uses a software-defined approach where the compiler orchestrates everything.

Groq's TSP can be thought of like a highly organized assembly line where every step is planned in advance, making it faster and more predictable than traditional GPUs, which are more like flexible but sometimes unpredictable workers. This design makes TSP particularly good at handling AI tasks quickly and efficiently.

Groq secured a $1.5 billion commitment from the Kingdom of Saudi Arabia to expand its delivery of AI chips and infrastructure to the country. This agreement was announced at LEAP 2025 and aims to advance Saudi Arabia's position as a global leader in AI computing infrastructure.
Groq has established a state-of-the-art data center in Dammam, Saudi Arabia, which is now delivering AI inference capabilities to customers worldwide through GroqCloud. The company built the region's largest inference cluster in just eight days in December 2024.
They announced a strategic partnership with GlobalFoundries to produce AI chips in upstate New York, aiming to challenge Nvidia's market dominance by offering competitive performance at lower costs.
Groq plans to deploy over 108,000 LPUs by the end of Q1 2025, making it the largest AI inference compute deployment by any non-hyperscaler. The company aims to provide at least 25 million tokens-per-second of compute capacity in Saudi Arabia by the end of Q1 2025.

These strategic moves demonstrate Groq's rapid expansion and its focus on establishing a strong presence in the global AI infrastructure market, particularly in Saudi Arabia.

Building on Groq:

You can get a free API key from https://console.groq.com/playground
Link to documentation

Install the Groq Python library:

pip install groq

Sample application:

import os

from groq import Groq

client = Groq(
    api_key=os.environ.get("GROQ_API_KEY"),
)

chat_completion = client.chat.completions.create(
    messages=[
        {
            "role": "user",
            "content": "Explain the importance of fast language models",
        }
    ],
    model="llama-3.3-70b-versatile",
)

print(chat_completion.choices[0].message.content)

Conclusion:

Groq is definitely a company to keep an eye on in the AI chip industry. They've come up with a clever new chip design that's super fast and efficient for AI tasks, especially for things like chatbots. With fresh funding, Groq is showing it's a serious contender. Their chips promise to be faster, cheaper, and more energy-efficient than the big players like Nvidia. While they're still the new kid on the block, Groq's innovative approach to AI inference could shake things up in a big way in the future as all the current development point to increased inference compute.

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