Why it matters
- 500-800+ tokens/second makes conversations and agent responses feel instantaneous — the most noticeable UX improvement available for open-model applications.
- OpenAI-compatible API means Groq is a drop-in replacement for many applications — change the base URL and model name, keep existing code.
- Free tier with no credit card makes it the default choice for developers prototyping open-model applications.
- Function calling support via specialized Llama models enables building production-ready agents on Groq's fast inference.
Key capabilities
- Ultra-fast inference: 500-800+ tokens/second for supported models.
- Open models: Llama 3.1, Mixtral, Gemma, Mistral, and more.
- OpenAI-compatible API: Drop-in replacement for most OpenAI SDK code.
- Free tier: Daily rate-limited access to all models; no credit card required.
- Function calling: Groq-fine-tuned Llama models with tool use capability.
- Whisper: High-speed Whisper speech-to-text via same API.
- Streaming: Real-time token streaming for chat applications.
- Low latency: First token in under 200ms for most requests.
Technical notes
- Hardware: Custom LPU (Language Processing Unit)
- Speed: 500-800+ tokens/second (varies by model and load)
- API: OpenAI-compatible REST; groq.com/docs
- Base URL:
https://api.groq.com/openai/v1
- Free tier: Rate-limited; no credit card
- Python:
pip install groq or use OpenAI SDK with base_url override
- Models: Llama 3.1, Mixtral 8x7B, Gemma, Whisper
Usage example
from groq import Groq
client = Groq(api_key="YOUR_GROQ_API_KEY")
response = client.chat.completions.create(
model="llama-3.1-70b-versatile",
messages=[{"role": "user", "content": "Explain the difference between TCP and UDP"}],
stream=True
)
for chunk in response:
print(chunk.choices[0].delta.content or "", end="")
Ideal for
- Developers building real-time chat interfaces and AI agents where response latency directly impacts UX.
- Prototyping with open models without managing GPU infrastructure or paying for premium model APIs.
- Applications that can tolerate open-model quality (Llama 70B) but need speed comparable to or faster than GPT-4o.
Not ideal for
- Applications needing GPT-4-level reasoning — Llama 70B is capable but below frontier for complex tasks.
- High-volume production at scale without rate limit concerns — evaluate paid plan limits carefully.
- Custom or fine-tuned model hosting — Groq runs their supported model list only.
See also
- Cerebras Chat — Comparable ultra-fast open model inference on WSE hardware; alternative to Groq.
- Fireworks AI — Fast open-source LLM inference; more model variety, slightly lower speeds.
- Together AI — Another open-source model hosting provider; different speed/cost tradeoffs.