Six-bit quantization (FP6) can effectively reduce the size of large language
models (LLMs) and preserve the model quality consistently across varied
applications. However, existing systems do not provide Tensor Core support for
FP6 quantization and struggle to achieve practical performance improvements
during LLM inference. It is challenging to support FP6 quantization on GPUs due
to (1) unfriendly memory access of model weights with irregular bit-width and
(2) high runtime overhead of weight de-quantization. To address these problems,
we propose TC-FPx, the first full-stack GPU kernel design scheme with unified
Tensor Core support of float-point weights for various quantization bit-width.
We integrate TC-FPx kernel into an existing inference system, providing new
end-to-end support (called FP6-LLM) for quantized LLM inference, where better
trade-offs between inference cost and model quality are achieved. Experiments
show that FP6-LLM enables the inference of LLaMA-70b using only a single GPU,
achieving 1.69x-2.65x higher normalized inference throughput than the FP16
baseline. The source code will be publicly available soon.

通过提出的 TC-FPx 全栈 GPU 核心设计方案，结合张量核心支持，为量化的大型语言模型推理提供全新的端到端支持（称为 FP6-LLM），实现了推理成本和模型质量之间的更好平衡。