Groq: The Inference Chip That Makes LLMs Run 10x Faster

Groq is an AI chip company whose Language Processing Unit (LPU) delivers dramatically faster LLM inference than GPU-based alternatives. While a typical GPU cluster might generate 30-50 tokens per second, Groq's hardware regularly exceeds 500 tokens per second — a 10-15x speed difference.

What makes Groq different

Most LLM inference runs on NVIDIA GPUs — hardware designed for parallel matrix operations that happen to work well for neural networks. Groq designed the LPU specifically for the sequential, memory-bandwidth-bound nature of LLM decoding:

• Deterministic execution — no dynamic scheduling means predictable, consistent latency
• On-chip SRAM — model weights live in on-chip memory, eliminating HBM memory bandwidth bottleneck
• Single-chip inference — no inter-GPU communication overhead

Groq Cloud API

Groq offers a cloud API at groq.com/cloud with OpenAI-compatible endpoints. Current models available:

When Groq speed actually matters

For most batch processing and async use cases, speed is irrelevant — the difference between 30 tok/s and 500 tok/s doesn't affect user experience. Groq's speed advantage matters when:

• Real-time voice AI — latency under 200ms total is required for natural conversation
• Live coding assistants — users are watching completion stream in real time
• High-frequency agentic loops — agents making dozens of sequential LLM calls per task
• Interactive demos — showing off AI speed as a product feature

Groq vs GPU cloud for cost

Groq's pricing is competitive with mid-tier GPU cloud options — not the cheapest, but not premium either. For Llama-3.3-70B specifically, Groq ($0.59/1M input) is cheaper than running the equivalent on dedicated A100 hardware while being 5-10x faster.

Groq is the right call when your bottleneck is generation speed, not model capability. For most production workloads, a slower but smarter model is the better tradeoff.

Using Groq with Tokoscope

Groq's API is fully OpenAI-compatible, so wrapping it with Tokoscope works out of the box:

import { wrap } from 'tokoscope'
import OpenAI from 'openai'

const client = wrap(new OpenAI({
  baseURL: 'https://api.groq.com/openai/v1',
  apiKey: process.env.GROQ_API_KEY
}), {
  apiKey: process.env.TOKOSCOPE_API_KEY
})

const response = await client.chat.completions.create({
  model: 'llama-3.3-70b-versatile',
  messages: [{ role: 'user', content: 'Hello' }]
})