The Test Setup
We benchmarked three workloads: single-turn Q&A (short input, short output), document summarization (long input, medium output), and multi-step reasoning (medium input, long output). All using Llama 3.1 70B, which both platforms support.
Latency results — time to first token: - Groq LPU: 42ms average (single-turn), 89ms (summarization), 67ms (reasoning) - NVIDIA A100 (vLLM): 340ms average (single-turn), 820ms (summarization), 510ms (reasoning)
Groq is 6-9x faster on time-to-first-token. That's not marketing — those are our numbers across 10,000 requests per workload.
Where It Gets Complicated
Throughput at scale is a different story. When we ramped to 500 concurrent requests, Groq's queue times increased significantly. The LPU architecture excels at individual request speed but the available capacity is still limited compared to NVIDIA's massive GPU cloud ecosystem.
Cost per token is where NVIDIA still wins for sustained workloads. Groq pricing works out to roughly 2.3x the cost of self-hosted A100s for sustained throughput. You're paying a premium for speed.
Model availability is Groq's biggest limitation. You get Llama variants and Mixtral. If your production system needs Claude or GPT-4, Groq isn't an option. For our enterprise clients, model selection is non-negotiable.
Our Recommendation
Use Groq for latency-sensitive, user-facing applications where speed directly impacts UX — chatbots, real-time search, interactive coding assistants. Use NVIDIA/cloud GPUs for batch processing, fine-tuning, and workloads where throughput matters more than individual request speed.
The future isn't Groq vs NVIDIA. It's intelligent routing between both based on workload characteristics.