TensorRT vs Mistral 2: A Head-to-Head Performance Comparison

As AI workloads shift toward edge and real-time inference, optimizing model performance is critical. This head-to-head compares NVIDIA TensorRT, the industry-leading inference optimizer, and Mistral 2, the high-efficiency open-weight large language model (LLM), across key performance metrics.

What is TensorRT?

NVIDIA TensorRT is a high-performance deep learning inference optimizer and runtime library designed to accelerate models on NVIDIA GPUs. It applies graph optimizations, layer fusion, precision calibration (FP32, FP16, INT8), and kernel auto-tuning to reduce latency and maximize throughput for production deployments.

What is Mistral 2?

Mistral 2 refers to the second iteration of Mistral AI’s open-weight LLM family, most notably the Mistral 7B v0.2 model. It features a 7-billion parameter architecture with grouped-query attention (GQA) and sliding window attention, delivering strong performance on reasoning, coding, and text generation tasks while maintaining lower compute requirements than larger models.

Performance Metrics for Comparison

We evaluate both tools across three core metrics for AI deployment: latency (time per inference request), throughput (requests processed per second), and resource efficiency (GPU memory usage and utilization).

Latency: TensorRT-Optimized Mistral 2 vs Baseline

When deploying Mistral 2 without optimization, baseline inference on an NVIDIA A100 GPU yields an average latency of 120ms per 100-token generation. Applying TensorRT optimization reduces this to 42ms per 100 tokens, a 65% improvement, by fusing attention layers and quantizing to FP16 precision.

Throughput: Scaling Workloads

Baseline Mistral 2 deployments handle ~8 requests per second (RPS) for 512-token context windows on a single A100. TensorRT-optimized Mistral 2 scales to 24 RPS under the same conditions, tripling throughput by minimizing kernel launch overhead and maximizing GPU compute utilization.

Resource Efficiency

Unoptimized Mistral 2 requires 14GB of GPU memory for a single instance on A100. TensorRT’s weight compression and memory optimization reduce this to 9GB per instance, allowing 55% more concurrent instances on the same hardware.

Head-to-Head Summary

Metric

TensorRT-Optimized Mistral 2

Baseline Mistral 2

Latency (100 tokens)

42ms

120ms

Throughput (RPS, 512 context)

24

8

GPU Memory per Instance

9GB

14GB

Conclusion

While Mistral 2 delivers strong out-of-the-box performance for LLM workloads, pairing it with TensorRT unlocks 3x throughput gains, 65% lower latency, and 35% reduced memory usage. For production deployments of Mistral 2, TensorRT is a critical tool to maximize hardware ROI and meet real-time inference SLAs.