Question 1

What LLMs are currently supported on the Groq platform?

Accepted Answer

Groq primarily supports open-source large language models optimized for LPU architecture, such as Llama 2 and Mixtral, with continuous expansion of the model catalog based on performance and developer demand.

Question 2

How does Groq's LPU architecture impact inference costs compared to GPUs?

Accepted Answer

Groq's LPU is designed for extreme inference efficiency, often resulting in lower per-token costs for high-throughput, low-latency workloads compared to general-purpose GPUs, especially for real-time agent interactions.

Question 3

Can I deploy custom fine-tuned models on Groq's LPUs?

Accepted Answer

Currently, Groq focuses on optimizing and serving a curated set of pre-trained and publicly available LLMs. Support for custom model deployment is a common request and is under evaluation for future platform enhancements.

Question 4

What are the typical latency figures I can expect for token generation?

Accepted Answer

For supported models, users can typically expect sub-100ms first-token latency and sustained token generation rates often exceeding hundreds of tokens per second, depending on the model and prompt complexity.

Question 5

What are the rate limits for API access, and how can they be increased?

Accepted Answer

Default API rate limits are applied per account and are detailed in the developer documentation. For increased throughput requirements, users can contact support to discuss custom rate limit adjustments based on their specific use case and projected consumption.

Groq Website Full Guide (2026)

Introduction

Key Features

Use Cases

Building Low-Latency AI Agent Response

How to Use Groq

Access the Developer Console

Groq Alternatives

LangChain

Fixie

Jarvis

Humanloop

About Groq

Official Website

Video Mentions

Groq Status