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Six-bit quantization (FP6) can effectively reduce the size of large languagemodels (LLMs) and preserve the model quality consistently across variedapplications. However, existing systems do not provide Tensor Core support forFP6 quantization and struggle to achieve practical performance improvementsduring LLM inference. It is challenging to support FP6 quantization on GPUs dueto (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 unifiedTensor Core support of float-point weights for various quantization bit-width.We integrate TC-FPx kernel into an existing inference system, providing newend-to-end support (called FP6-LLM) for quantized LLM inference, where bettertrade-offs between inference cost and model quality are achieved. Experimentsshow 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 FP16baseline. The source code will be publicly available soon.

FP6-LLM: Efficiently Serving Large Language Models Through FP6-Centric Algorithm-System Co-Design