Model Optimizer Benchmark Reference
December 7, 2025 · View on GitHub
This document summarizes performance and accuracy measurements of Model Optimizer for a few popular models. The benchmark in the following tables is provided as reference points and should not be considered as the peak performance that can be delivered by Model Optimizer. All performance numbers are tested with TensorRT-LLM or TensorRT.
1. Post-training quantization (PTQ) for LLMs
1.1 Performance
Config: H200, nvidia-modelopt v0.21.1, TensorRT-LLM v0.15, latency measured with trtllm-bench. Inference speedup are compared to the BF16 baseline. Speedup is normalized to the GPU count.
Benchmark scenario: Input tokens 2048, output tokens 128. Real performance may vary based on the target usecases and flags used to build the TensorRT-LLM engine.
Memory saving is not reported here as TensorRT-LLM occupies all the remaining available GPU memory for KV caching.
If the GPU memory is the limitation, lower bit quantization may have better GPU-count-normalized throughput gain with fewer TP.
| BF16 (8B:TP1, 70B:TP2) | FP8 (TP1) | INT4 AWQ (TP1) | W4A8 AWQ (TP1) | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Model | Batch Size | Tokens/sec | Tokens/sec | Speedup | Tokens/sec | Speedup | Tokens/sec | Speedup | |||
| Llama3.1-8B | 1 | 173.80 | 245.03 | 1.41x | 231.75 | 1.33x | 239.70 | 1.38x | |||
| 8 | 803.11 | 1,051.17 | 1.31x | 599.72 | 0.75x | 801.72 | 1.00x | ||||
| 64 | 1,679.74 | 2,190.93 | 1.30x | 1,392.78 | 0.83x | 1,930.86 | 1.15x | ||||
| Llama3.1-70B | 1 | 45.81 | 43.46 | 1.90x | 44.10 | 1.93x | 46.31 | 2.02x | |||
| 8 | 182.61 | 182.07 | 1.99x | 93.98 | 1.03x | 140.02 | 1.53x | ||||
| 64 | 401.50 | 420.64 | 2.10x | 176.68 | 0.88x | 345.43 | 1.72x |
1.2 Accuracy
The table below shows the MMLU loss in percentage compared to BF16 baseline. Config: H100, nvidia-modelopt v0.21.1, TenorR-LLM v0.15. Note that typically FP8 is the go-to choices for H100. 4-bit AWQ methods is recommended when GPU memory is a constraint. More benchmark with earlier version of Model Optimizer can be found in this TensorRT-LLM README.
| Model | MMLU loss FP8 | MMLU loss INT4 AWQ | MMLU loss W4A8 AWQ |
|---|---|---|---|
| Llama3.1-8B (instruct) | 1.50% | 5.66% | 6.00% |
| Llama3.1-70B (instruct) | 0.38% | 1.07% | 1.20% |
2. PTQ for Stable Diffusion
The following table shows inference speedup for INT8 and FP8 on a Stable Diffusion XL 1.0 base model compared to the FP16 baseline. Config: Image resolution=1024×1024, 30 steps. TensorRT v9.3. num-warmup-runs=1. Batch size=1.
| GPU | INT8 Latency (ms) | FP8 Latency (ms) | Speedup (INT8 v.s. FP16) | Speedup (FP8 v.s. FP16) |
|---|---|---|---|---|
| RTX 6000 Ada | 2,479.19 | 2,441.16 | 1.43x | 1.45x |
| RTX 4090 | 2,058.11 | 2,161.38 | 1.20x | 1.14x |
| L40S | 2,338.88 | 2,167.82 | 1.25x | 1.35x |
3. Quantization-aware training
The below table demonstrates the validation loss of Quantization-aware training (QAT) compared to PTQ of a Llama 2 7B model using nvidia-modelopt v0.11.0. The baseline is fine-tuned on the target dataset. Note that we use INT4 to showcase that QAT can better preserve model accuracy at low precision. This implies that QAT can be applied with a low training cost, enabling generative AI applications that are sensitive to accuracy drop to preserve accuracy even at ultra-low precisions where both weight and activations are 4-bit for NVIDIA Blackwell platform.
| Method | Dataset | Val loss - BF16 Baseline | Val loss - PTQ | Val loss - QAT (lower is better) |
|---|---|---|---|---|
| INT4 Weight, FP16 Activation | samsum | 1.036 | 1.059 | 1.044 |
| INT4 Weight, INT8 Activation | samsum | 1.036 | 3.321 | 1.294 |
| INT4 Weight, FP16 Activation | databricks-dolly-15k | 1.151 | 1.305 | 1.172 |
| INT4 Weight, INT8 Activation | databricks-dolly-15k | 1.151 | 2.313 | 1.640 |
4. Sparsity
4.1 Performance
The table shows the inference speedup of a sparsified Llama 2 70B model compared to the baseline dense model in different batch sizes. The benchmark with batch_size=896 is part of MLPerf Inference v4.0. Config: NVIDIA H100 80GB GPU. FP8, TP=1, PP=1 for all sparsified models. The dense model needs TP=2 due to larger weight sizes.
| Batch Size | Inference speedup (compared to the FP8 dense model) |
|---|---|
| 32 | 1.62x |
| 64 | 1.52x |
| 128 | 1.35x |
| 896 | 1.30x |
4.2 Accuracy
We recommend using sparsity with fine-tuning to avoid accuracy degradation. The following table shows the comparison of validation loss of a Llama 2 70B using sparsity with and without fine-tuning. Finetuning and validation are done on the Open-Orca dataset.
| Method | Validation loss (lower is better) |
|---|---|
| FP8 (baseline) | 0.721 |
| FP8 + SparseGPT, no fine-tuning | 2.724 |
| FP8 + Sparsity, with fine-tuning | 1.01 |