The deployment of large language models (LLMs) across heterogeneous environments requires format-specific conversion, precision tuning, and consistent evaluation-tasks that are often fragmented across multiple tools. This work presents SOLO-Export, a unified command-line interface (CLI) framework for multi-format export and post-export benchmarking of causal LLMs. Having precision options for FP16 and INT8 where appropriate, the system supports the ONNX, TorchScript, Hugging Face, TensorFlow Lite, and TensorRT backends. Device-aware exports for both CPU and CUDA targets are made possible by a configuration-driven workflow that generates artifacts in a uniform directory structure. Each exported model is benchmarked using the Penn Treebank dataset by the integrated evaluation harness, which reports inference latency, token-level accuracy, and perplexity. According to experimental results, FP16 exports on GPU-oriented backends like TensorRT achieved up to 3.2 times lower latency than baseline FP32 models. On the top of that, with minimal impact on perplexity, storage size was reduced by more than 60% thanks to INT8 quantization. The combined approach reduces manual configuration overhead, speeds up deployment preparation, and ensures consistent performance insights across formats. This study demonstrates that a single, scalable pipeline can be effective.