Neural Magic has released the LLM Compressora state-of-the-art tool for optimizing large language models that enables much faster inference through much more advanced model compression. The tool is therefore an important component in Neural Magic’s quest to make high-performance open source solutions available to the deep learning community, especially within the vLLM framework.
LLM Compressor alleviates the pain points that arise from the previous fragmented landscape of model compression tools, where users had to develop multiple custom libraries similar to AutoGPTQ, AutoAWQ, and AutoFP8 to apply certain quantization and compression algorithms. LLM Compressor integrates these fragmented tools into a single library to easily apply state-of-the-art compression algorithms such as GPTQ, SmoothQuant, and SparseGPT. These algorithms are implemented to create compressed models that offer reduced inference latency while maintaining high levels of accuracy, which is critical for bringing the model into production environments.
The second key technical advancement brought by the LLM compressor is support for activation and weight quantization. In particular, activation quantization is important to ensure that the INT8 and FP8 tensor cores are used. These are optimized for high-performance computing on new NVIDIA GPU architectures such as the Ada Lovelace and Hopper architectures. This is an important capability to accelerate compute-bound workloads, where the computational bottleneck is alleviated by using lower-precision arithmetic units. This means that by quantizing activations and weights, the LLM compressor enables up to a 2x increase in performance for inference tasks, mostly under high server loads. This is attested by large models such as Llama 3.1 70B, which demonstrates that by using the LLM compressor, the model achieves latency performance very close to that of a non-quantized version running on four GPUs with only two.
In addition to activation quantization, the LLM compressor supports state-of-the-art 2:4 weight pruning with structured sparsity using SparseGPT. This weight pruning selectively removes redundant parameters to reduce accuracy loss by reducing model size by 50%. In addition to speeding up inference, this combination of quantization and pruning minimizes memory usage and enables deployment on resource-constrained hardware for LLMs.
The LLM compressor was designed to be easily integrated into any open source ecosystem, in particular the Hugging Face model hub, through easy loading and execution of compressed models within vLLM. Furthermore, the tool extends this by supporting a variety of quantization schemes, including fine-grained control of quantization such as per-tensor or per-channel on weights and per-tensor or per-token on activation. This flexibility in quantization strategy will allow for very fine-tuning in relation to the performance and accuracy demands of different models and deployment scenarios.
Technically, the LLM compressor is designed to work with multiple model architectures with scalability. There is an aggressive roadmap for the tool, which includes expanding support for MoE models, vision language models, and non-NVIDIA hardware platforms. Other areas of the roadmap to be developed include advanced quantization techniques such as AWQ and tools to create non-uniform quantization schemes; these are expected to further expand model efficiency.
In conclusion, LLM Compressor thus becomes an important tool for both researchers and practitioners when optimizing LLMs for production deployment. It is open source and has state-of-the-art features, making it easy to compress models and achieve significant performance improvements without affecting model integrity. LLM Compressor and other similar tools will play a very important role soon as ai continues to scale to efficiently deploy large models on various hardware environments, making them more accessible for application in many other areas.
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