Groq LPU: How to Get 800+ Tokens/sec LLM Inference

What Is an LPU?

Groq's Language Processing Unit (LPU) is purpose-built for sequential computation - specifically the token-by-token generation process that makes GPU-based LLM inference slow. GPUs excel at massive parallel matrix multiplication (training), but autoregressive generation is inherently sequential. The LPU's architecture eliminates the memory bandwidth bottleneck that limits GPU inference speed.

Result: 800+ tokens/second on Llama 3.1 70B - compared to 40-80 tokens/second on a typical A100 GPU.

Why Speed Matters

At 40 tokens/sec, a 500-token response takes 12.5 seconds - too slow for interactive chat or real-time voice applications. At 800 tokens/sec, that same response completes in 0.6 seconds. The difference between "feels like waiting" and "feels instantaneous."

For streaming use cases (code generation, long-form writing), higher throughput directly improves user experience.

GroqCloud: Free Tier Available

GroqCloud offers a free tier with rate limits suitable for development and experimentation. Supported models include:

• llama-3.1-70b-versatile - 800+ tokens/sec
• llama-3.1-8b-instant - 1200+ tokens/sec
• mixtral-8x7b-32768 - 500+ tokens/sec
• gemma2-9b-it - 1000+ tokens/sec

Drop-in OpenAI Replacement

The Groq API is compatible with the OpenAI Python SDK - change two lines to switch:

from groq import Groq

# Replace: client = OpenAI()
client = Groq(api_key="your-groq-api-key")

response = client.chat.completions.create(
    # Replace: model="gpt-4o-mini"
    model="llama-3.1-70b-versatile",
    messages=[
        {"role": "user", "content": "Write a merge sort in Python."}
    ],
    max_tokens=1024,
)
print(response.choices[0].message.content)

Streaming for Real-Time Applications

from groq import Groq

client = Groq()

stream = client.chat.completions.create(
    model="llama-3.1-70b-versatile",
    messages=[{"role": "user", "content": "Explain quantum entanglement step by step."}],
    stream=True,
)

for chunk in stream:
    delta = chunk.choices[0].delta.content
    if delta:
        print(delta, end="", flush=True)

Latency Comparison Table

Install the groq-python Library

pip install groq

The library mirrors the OpenAI SDK's interface - if you've used openai-python, groq-python will feel identical.

Batch vs Streaming

For user-facing features, always stream - users see content appearing immediately rather than waiting for the full response. For background jobs (summarization pipelines, classification batches), non-streaming is fine and slightly simpler to implement.

Summary

Groq LPU makes 70B models feel as fast as 7B models on GPU. For latency-sensitive applications - chat, code completion, voice AI - GroqCloud is the fastest inference option available today. Sign up at console.groq.com and explore the SDK at groq/groq-python.