Groq vs NVIDIA TensorRT: AI Inference Speed Compared
Two approaches to fast AI inference go head-to-head. We compare Groq's custom LPU hardware against NVIDIA's TensorRT software optimization on GPUs.
The Inference Speed Race
AI inference speed directly impacts user experience, cost-efficiency, and the types of applications you can build. Two fundamentally different approaches compete for the crown: Groq's custom Language Processing Unit (a purpose-built chip) and NVIDIA's TensorRT (software optimization for existing GPU hardware).
This comparison matters because the choice between them affects infrastructure decisions, costs, and architectural constraints for any AI-powered application.
Raw Speed Benchmarks
On Llama 4 Scout (17B): Groq delivers 520 tokens/second versus TensorRT on A100 at 185 tokens/second—a 2.8x advantage. Time-to-first-token is 12ms (Groq) versus 45ms (TensorRT). For smaller models, Groq's advantage grows to 4-5x.
However, TensorRT on the newer H200 narrows the gap significantly: 310 tokens/second with 28ms TTFT. And TensorRT supports larger models that don't fit on Groq hardware—for 70B+ models, TensorRT on multi-GPU setups is the only option.
Cost Comparison
Groq's API pricing ($0.05/M input tokens for Scout) is cheaper than equivalent GPU hosting for most workloads. However, GPU-based inference offers more flexibility—you can run any model, switch between models without cold starts, and scale horizontally.
For dedicated, high-volume workloads with supported models, Groq is more cost-effective. For varied workloads requiring model flexibility, TensorRT on GPU infrastructure provides better value despite higher per-token costs.
Flexibility and Ecosystem
TensorRT supports virtually any model architecture and integrates with the massive NVIDIA ecosystem—CUDA libraries, Triton Inference Server, and thousands of optimized models. Groq supports a limited but growing list of models and requires specific model formats.
For research teams and companies running diverse model portfolios, TensorRT's flexibility is essential. For production deployments serving a single model at scale, Groq's simplicity and speed are compelling.
Recommendation
Choose Groq for: real-time applications requiring minimum latency, high-volume inference of supported models, and cost-sensitive deployments where speed is a feature. Choose TensorRT for: model flexibility, frontier model inference, multi-modal workloads, and research environments.
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