Neural Magic has introduced the LLM Compressor, a new toolkit designed to enhance the performance of large language models (LLMs) through model compression techniques such as quantization and pruning. The library promises up to three times faster throughput and significantly reduced latency, making it a key tool for developers looking to streamline AI applications.
The LLM Compressor unifies various compression strategies, including GPTQ, SmoothQuant, SparseGPT, and RTN, under a single framework. This approach aims to simplify the process of creating smaller, faster, and efficient models. It supports popular models like Llama 3.1, directly integrating with the vLLM platform for seamless application of compressed models.
A significant advancement introduced by the LLM Compressor is activation quantization, which when used in conjunction with weight quantization, can leverage faster INT8 and FP8 tensor cores. This dual quantization strategy allows for performance improvements, particularly in high-load environments where rapid response times are crucial. For instance, a model quantized with both weight and activation layers can handle more queries per second without compromising on latency or accuracy.
Neural Magic’s new toolkit also incorporates a variety of customization options, such as per-tensor or per-channel quantization, and supports emerging hardware platforms, extending beyond Nvidia’s GPUs. This flexibility ensures that developers can adapt the compression settings to best fit their specific use cases and hardware configurations.
The company is actively expanding its LLM Compressor with plans to include support for Mixture of Experts (MoE) models, various quantization schemes like AWQ, and more. Additionally, Neural Magic is engaging with other hardware providers to broaden the toolkit’s applicability, making it a versatile choice for developers across different platforms.
For those interested in leveraging this technology, Neural Magic provides detailed documentation and examples on how to integrate LLM Compressor into existing projects. This includes example code for applying different quantization strategies to models and benchmarking them under various conditions.