FP6 LLM : Efficiently serving large language models

FP6 LLM

AI Model AI Model Inference Training #Large language models #GPU inference #Quantized models Standard Picks Open Source

Overview :

FP6-LLM is a new supporting solution for large language models. Through six-bit quantization (FP6), it effectively reduces the model size while maintaining model quality across various applications. We present TC-FPx, the first complete GPU kernel design that uniformly supports various quantization bit widths for floating-point weights. By integrating the TC-FPx kernel into existing inference systems, we provide a new end-to-end support for quantized LLM inference (called FP6-LLM), achieving a better balance between inference cost and model quality. Experiments demonstrate that FP6-LLM enables inference of LLaMA-70b using a single GPU, achieving normalized inference throughput 1.69x to 2.65x higher than the FP16 baseline.

Target Users :

Suitable for inference scenarios requiring large language model support, especially when there are strict requirements for inference cost and model quality.

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Use Cases

Research institutions use FP6-LLM for large-scale language model inference

Software companies integrate FP6-LLM into their natural language processing applications

Data centers leverage FP6-LLM to accelerate large-scale language model inference

Features

Six-bit model support

Unified support for various quantization bit widths of floating-point weights

Provide end-to-end support, achieving a better balance between inference cost and model quality