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Groq

What it is

Groq is an AI infrastructure company that developed the Language Processing Unit (LPU), a new type of processor designed specifically for the extreme high-speed requirements of LLMs.

What problem it solves

Solves the "bottleneck" of slow LLM inference, providing near-instantaneous responses that enable real-time applications and highly interactive agents.

Where it fits in the stack

Inference Provider / Infrastructure. It provides a high-speed API for the most popular open-source models (Llama, Mixtral, Gemma).

Typical use cases

  • Real-time Agents: Voice assistants or interactive chatbots that require sub-second response times.
  • High-Volume Processing: Summarizing or analyzing large quantities of text at hundreds of tokens per second.
  • Interactive Coding: Powering coding assistants where immediate, fluid feedback is essential.

Getting started

Install the SDK: 

pip install groq

Basic API call (Python): 

from groq import Groq

client = Groq()

chat_completion = client.chat.completions.create(
    messages=[
        {
            "role": "user",
            "content": "Explain the importance of low latency in AI.",
        }
    ],
    model="llama3-70b-8192",
)

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

Strengths

  • Extreme Speed: Often 10x+ faster than traditional GPU-based providers (400-800+ tokens/sec).
  • Open Model Support: Focuses on the best open-weights models like Llama 3 and Mixtral.
  • Low Latency: Unmatched time-to-first-token (TTFT) and overall throughput.
  • Pricing Tiers: Provides a generous Free tier for development and prototyping, alongside competitive usage-based On-Demand pricing.

Limitations

  • Model Selection: Limited to the open models they have specifically optimized for their LPU hardware.
  • Context Window: Historically had smaller context windows than cloud giants, though this is expanding rapidly.

When to use it

  • When response speed is the absolute top priority.
  • For "agentic" workflows where an agent makes many sequential, recursive LLM calls.
  • When using Llama or Mistral models and looking for the fastest possible user experience.

When not to use it

  • If you need proprietary models like GPT-4 or Claude 3.5.
  • For extremely large context tasks (e.g., 200k+ tokens) that may exceed current LPU memory limits.

Licensing and cost

  • Open Source: No (Proprietary hardware and LPU-optimized software stack).
  • Cost: Paid (Usage-based), Free tier available for development.
  • Self-hostable: No (Cloud service).

Sources / References

Contribution Metadata

  • Last reviewed: 2026-03-03
  • Confidence: high