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NVIDIA Model Optimizer — quantization, pruning, distillation, and speculative decoding for accelerating LLMs, diffusion models, and vision models on NVIDIA GPUs.
Passed 17 of 21 test cases with the skill, and 11 of 21 without it.
and 6 more in the full report
7 fixed11 passed either way4 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 6 test cases the skill fixed. Same model, same prompt, with and without the skill.
From which exact subpackage in ModelOpt is the knowledge distillation interface imported?
| Checked | Without skill | With skill |
|---|---|---|
| Imports modelopt.torch.distill | Missed | Passed |
The instructions your model reads when the skill loads.
NVIDIA Model Optimizer provides state-of-the-art model optimization techniques — quantization (FP8, INT4, NVFP4), pruning, knowledge distillation, and speculative decoding — for models deployable on TensorRT-LLM, SGLang, and vLLM.
uv pip install nvidia-modelopt
import modelopt.torch.quantization as mtq
# FP8 post-training quantization
quant_cfg = mtq.FP8_DEFAULT_CFG
mtq.quantize(model, quant_cfg, forward_loop=calib_loop)
# Export for TensorRT-LLM
from modelopt.torch.export import export_tensorrt_llm_checkpoint
export_tensorrt_llm_checkpoint(model, "model.pt", dtype="fp8")
import modelopt.torch.pruning as mtp
import modelopt.torch.distill as mtd
# Prune
pruned = mtp.prune(model, ratio=0.3, structure="2:4_sparse")
# Distill
teacher = load_teacher_model()
student = mtd.distill(student_model, teacher, kal="logit", alpha=0.5)
test cases passed, vs 11 / 21 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.