Groq Review 2026: The Inference Speed Demon That Changed Expectations

TL;DR

Groq builds custom inference chips — LPUs — and runs open-source models on them. The result: hundreds to thousands of tokens per second on models that would do 50-100 on a top GPU. In 2026 Groq is the obvious pick for voice agents, real-time agents, and any product where latency is the user experience.

What it does

• LPU hardware — custom inference chips optimized for token throughput
• GroqCloud — managed inference on LPUs
• Open model serving — Llama 3.3, DeepSeek, Whisper, etc.
• OpenAI-compatible API — drop in by changing the base URL
• Speech models for STT and translation

What is great

Speed is unmatched. Llama 3.3 70B at 250+ tokens/sec on Groq versus 70-100 on GPU competitors. For interactive AI this transforms UX.

Whisper and STT performance is extraordinary — useful for live captioning and voice agents.

OpenAI-compatible API. Switch base URL, ship.

Free tier exists — enough to prove the speed and prototype.

What is not

Limited model catalog. Only models that have been compiled for LPUs — you cannot just push any open weight.

Capacity is a recurring story. Demand has outstripped supply at times. Reliability has improved.

Open models only. No GPT-5 or Claude here.

Pricing competitive but not always cheapest. You pay for the speed.

Pricing

Per-token examples:

• Llama 3.3 70B: ~$0.59 input / $0.79 output per 1M tokens
• Llama 3.1 8B: ~$0.05 per 1M tokens
• Whisper Large v3: ~$0.111/hour audio

Verdict

For any product where speed is the experience — voice agents, real-time chat, agentic loops with many turns — Groq is the right pick. For batch or quality-first workloads, Fireworks or Together. For frontier intelligence, OpenAI or Anthropic. Groq owns latency.

Who it is for

Best for: Voice agents, real-time chat, and any product where token latency drives UX.

Not for: Teams needing frontier model quality or maximum catalog breadth.