In the world of AI, speed isn't just a feature—it's a fundamental requirement for creating truly interactive, human-like experiences. For too long, we’ve accepted a user-perceived delay, or latency, as a necessary evil. The pause after you ask a question, the slow drip of a response from a Large Language Model (LLM)—these moments of friction break the illusion of real-time intelligence and hinder the creation of a new class of applications. We've seen this in everything from voice assistants to automated customer service, where a delay of even a few hundred milliseconds can degrade the user experience from "magical" to "just another bot".
This is the problem we're here to solve. This post is for the engineers, architects, and product managers who are ready to move past the status quo. We'll provide a comprehensive, data-driven look at Groq, an AI inference platform that has been purpose-built to eliminate latency. Our goal is to help you understand what Groq is, how its unique architecture works, what its real-world performance looks like, and how you can use it today to build a real-time Coding AI bot or other AI Agents. By the end of this deep dive, you will have the knowledge to make an informed decision on whether Groq is the right tool for your next project.
To understand Groq, we must first look past the marketing and into the core architecture. At its heart, Groq is not a software company or a simple API provider; it's a hardware company that has built a specialized processor for AI inference. This processor, the Language Processing Unit (LPU), is a fundamental departure from the general-purpose GPUs that dominate the market today. While GPUs are highly efficient for the massively parallel workloads of AI training, they are not optimized for the sequential, single-stream nature of inference. This is where the LPU shines.
The LPU's performance is a result of four core design principles:
A Software-First Approach: Unlike traditional hardware, where the software is often an afterthought, Groq's compiler is the central intelligence. It pre-computes the entire execution graph, including inter-chip communication patterns, down to the individual clock cycles. This allows the software to have complete, deterministic control over every step of the inference process, eliminating non-deterministic delays and resource bottlenecks.
A Programmable Assembly Line: The LPU operates like a deterministic assembly line. Data and instructions flow between functional units on "data conveyor belts" with no waiting or contention for resources. This streamlined process, both within and across chips, is a major improvement over the "hub and spoke" model of GPUs and is the key to its predictable performance.
On-Chip Memory (SRAM): One of the most significant bottlenecks in traditional GPU inference is the constant shuttling of model weights between the processor and slower, off-chip memory (HBM or DRAM). The LPU solves this by integrating hundreds of megabytes of on-chip SRAM as its primary weight storage, not just a cache. With a memory bandwidth of up to 80 terabytes per second, this design gives the LPU a 10x speed advantage over GPUs and enables compute units to pull in weights at full speed.
Deterministic Execution: The pre-computed nature of the LPU's execution model eliminates the hardware queues, reorder buffers, and runtime coordination delays that introduce "jitter" and non-deterministic latency in dynamically scheduled systems. This predictability is a crucial factor for real-time applications where consistent response times are paramount, such as autonomous vehicles or financial trading systems.
This unique architecture translates into real-world performance that often redefines the baseline for what's possible. For example, in a third-party benchmark by ArtificialAnalysis.ai, Groq's Llama 2 Chat (70B) API achieved a throughput of 241 tokens per second, which was "more than double the speed of other hosting providers" at the time. In more recent evaluations, the platform has demonstrated impressive speeds for a variety of models, including:
Llama 3 8B: 1,345 tokens/second
Llama 3 70B: 330 tokens/second
Qwen3 32B: 662 tokens/second
GPT OSS 20B: 1,000+ tokens/second
A real-world case study from a financial insights company, Fintool, provides a tangible example of this impact. After transitioning its query understanding and classification operations from gpt-4o to Groq's Llama 3.3 70B model (Llama is a Model created by Meta, in this case, hosted in Groq), Fintool saw an overnight chat speed increase of 7.41x, while its cost per token dropped by 89%. These kinds of performance gains are not incremental; they are a transformative step change that unlocks entirely new use cases.
The following tables provide a clear architectural and performance comparison.
| Feature | Groq (LPU) | Traditional GPU (e.g., NVIDIA H100) |
| Architecture | Tensor Streaming Processor (TSP) | General-Purpose GPU (GPGPU) |
| Primary Memory | On-chip SRAM (up to 230 MB/chip) | Off-chip HBM/DRAM (up to 80 GB/GPU) |
| Memory Bandwidth | Upwards of 80 TB/s (on-die) | Up to 8 TB/s (off-chip) |
| Scheduling | Static, Deterministic | Dynamic, Probabilistic |
| Execution Model | Programmable Assembly Line | Multi-core "Hub and Spoke" |
| Latency | Ultra-low, consistent (sub-millisecond) | Variable, with jitter at low batch sizes |
| Optimal Workload | Inference (low-batch), real-time applications | Training (high-batch), general-purpose compute |
| Energy Efficiency | Up to 10x more energy-efficient for inference | Optimized for mixed workloads, less efficient for inference |
| AI Model | Current Speed (Tokens/Second) | Input Token Price (Per Million Tokens) | Output Token Price (Per Million Tokens) |
Llama 3 8B 8k |
1,345 | $0.05 | $0.08 |
Llama 3 70B 8k |
330 | $0.59 | $0.79 |
Mistral Saba 24B |
330 | $0.79 | $0.79 |
DeepSeek R1 Distill Llama 70B |
400 | $0.75 | $0.99 |
GPT OSS 20B 128k |
1,000 | $0.10 | $0.50 |
GPT OSS 120B 128k |
500 | $0.15 | $0.75 |
While Groq is celebrated for its speed, a deeper analysis reveals a nuanced performance characteristic that developers must understand. Several third-party evaluations indicate a "dramatic" increase in Time to First Token (TTFT) when processing very large input contexts, for example, moving from 1,000 to 10,000 tokens. This seems counterintuitive given Groq's core focus on latency.
The reason for this behavior lies in the nature of LLM inference itself. The process consists of two main phases: the prefill phase, where the initial input prompt is processed, and the decoding phase, where output tokens are generated sequentially. Groq's LPU architecture is overwhelmingly optimized for the decoding phase, where its on-chip SRAM and static scheduling eliminate the memory bandwidth and execution bottlenecks that slow down GPUs. However, the prefill phase is still a sequential process where the initial input, regardless of hardware, must be processed. While Groq's compiler and fast memory help, the computational cost of running a very large context through a transformer network is a linear-time operation that can still impact TTFT.
The practical takeaway for developers is that Groq’s transformative speed is most pronounced in applications that require real-time, token-by-token generation with a short to medium input context. For heavy Retrieval-Augmented Generation (RAG) applications that process massive documents in a single prompt, the TTFT will increase, but the subsequent output generation speed remains unmatched. To mitigate this, a useful strategy is "Prompt Chaining," where complex tasks are decomposed into smaller subtasks, and the output of one prompt feeds into the next, thus keeping individual prompt lengths short and latency low. This is not a flaw in the architecture but a critical engineering tradeoff that developers need to be mindful of.
The speed and low latency of Groq's platform are not just theoretical advantages; they enable a new category of real-time applications that were previously impractical. A perfect example is a Coding AI bot that provides instant feedback, refactoring, and documentation—a far more interactive experience than existing solutions. Here is a practical, step-by-step plan for how we can build one using Groq's API and our popular CodeGPT VSCode extension.
Before we write any code, we must validate our idea. The ideal use case for a Groq-powered coding assistant is any task where a low-latency, real-time response is critical. Think of a bot that provides live, line-by-line code completion, helps a developer quickly refactor a function, or generates documentation on the fly. These are not tasks that should involve waiting for a slow API call. We can leverage an existing tool like the CodeGPT extension in VS Code, which follows a "bring your own key" model, making it a perfect fit for integrating with GroqCloud.
This phase outlines the concrete steps to get your project up and running. A small team of one or two developers can realistically complete this in one to two days.
Getting Your API Key: First, we need to access the GroqCloud platform. Simply navigate to the GroqCloud Console, sign up for an account, and create a new API key. It is best practice to configure this key as an environment variable to enhance security and streamline usage.
VS Code Integration with CodeGPT: We'll now connect our new API key to our development environment. Open Visual Studio Code and install the CodeGPT extension from the Marketplace. Once installed, find the CodeGPT extension settings. Under "AI Providers," we can select Groq and paste our API key. This process is straightforward and requires no complex configuration.
Your First API Call: Groq's API is intentionally designed to be OpenAI-compatible. This means that developers can migrate existing applications from other providers by changing just a few lines of code. Below is a simple Python example that demonstrates a chat completion request using the official Groq Python library. We can see how the
api_key, api_base_url, and model parameters are all that need to be changed to get started.
import os
from groq import Groq
# 1. Initialize the Groq client, using the API key from environment variables
client = Groq(
api_key=os.environ.get("GROQ_API_KEY"),
)
# 2. Make an API call to create a chat completion
chat_completion = client.chat.completions.create(
messages=,
# 3. Specify the model to be used
model="llama3-8b-8192",
)
# 4. Print the AI-generated response
print(chat_completion.choices.message.content)
# To view the full response object
# print(chat_completion)This simple example shows that with Groq's API and a tool like CodeGPT, we can build a functional AI Coding assistant in a matter of minutes.
It's important to recognize that Groq’s platform extends far beyond simple text generation. Its speed and low latency are foundational for a new class of AI Agents that can perform complex, multi-step tasks in real-time. The platform offers specialized "Compound AI Systems" like compound-beta and compound-beta-mini which are designed to intelligently use external tools, such as web search and code execution, in a single API call.
This capability is a direct response to the limitations of traditional LLMs. A plain model can generate text, but it cannot access current information or perform complex calculations. Groq's compound-beta models, powered by E2B's secure sandboxed environments, can autonomously decide to perform a web search to find current information or execute Python code to solve a computational problem before generating a final response. This is a critical development for any real-time agent, including a
Coding AI bot, which needs to be able to look up documentation for a library or validate a code snippet without introducing unacceptable latency. This architectural choice demonstrates a clear strategic move from providing just fast inference to a full-stack platform that empowers developers to build more capable and interactive AI agents.
While Groq's technology is undeniably a game-changer for many AI applications, a responsible expert-level analysis requires a candid look at its tradeoffs and the competitive landscape. As a trusted advisor, we must acknowledge that no technology is a silver bullet.
The Cerebras Challenge: The AI hardware market is not static, and new players are emerging as fierce competitors. Cerebras, another specialized chip company, has recently released an inference service that, according to their claims, is 2.4 times faster than Groq for the Llama 3.1 8B model. While Groq's low latency remains a core differentiator, this demonstrates that competition for raw speed is intensifying. Online developer discussions also highlight a key difference: while Groq excels at low-batch, single-user latency, some argue that Cerebras's wafer-scale chips are better optimized for massive throughput and training workloads on gigantic models.
Business Realities: Despite securing major deals and funding, Groq has faced challenges. Reports from mid-2025 indicated that Groq had revised its 2025 revenue projections from over $2 billion to around $500 million. While the company is actively seeking additional funding, this suggests the company is facing volatility and fierce competition from established hyperscalers like Amazon, Google, and Microsoft, who are also developing and using their own custom AI chips.
Inference-Only Focus: Groq's LPU is purpose-built for inference, and it is not a platform for training new, large-scale models. This is a crucial distinction for organizations that need a full-stack solution for both training and deployment. For those use cases, a general-purpose solution like NVIDIA's H100 GPUs may still be a better fit due to its ecosystem maturity and versatility.
We've compiled some of the most common questions developers and project managers ask when evaluating a new technology.
Q: Why would I use Groq instead of an industry giant like OpenAI? A: The primary reason is unparalleled speed and cost-effectiveness for a specific class of problems (now you can use OpenAI's OpenSource models OSS in Groq). Groq offers a step-change in performance for real-time applications that require instant responses, a task where other providers often fall short due to their reliance on general-purpose hardware. For a supported open-source model, Groq provides a clear cost-per-token advantage without sacrificing speed or quality.
Q: What is the difference between Groq and the Grok chatbot? A: This is a common point of confusion. Groq (pronounced "grok") is an AI hardware company that creates the specialized LPU chip and a platform for fast AI inference. Grok is a conversational AI model developed by xAI, an independent company founded by Elon Musk. They are completely separate entities.
The future of AI is not just about intelligence; it's about speed. Groq is a specialized and powerful tool that is uniquely positioned to solve the latency problem for a wide range of real-time applications, from real-time financial insights to instant Coding AI bots. Its LPU architecture, agentic capabilities, and cost efficiency make it a compelling choice for developers who are ready to build the next generation of interactive, high-performance AI. While the company operates in a competitive and evolving market, its core technology provides a transformative advantage that is difficult for general-purpose solutions to match.
The future of real-time AI is here. Get your Groq API key and start building today.