2-rd Transformer Engine 最重磅莫过于新一代 Transformer Engine 支持 FP4 精度。FP4 到底是 training/inference 还是 only-inference? 官网模棱两可,个人更偏向带 training。前一代 Transformer Engine 便是用启发式算法做 auto mixed precision FP32/BP16 训练提升性能,在上一代的技术积累下能做到 FP4 训练也不...
GB200 NVL72 presenta capacidades de vanguardia y un Transformer Engine de segunda generación que habilita FP4 AI y, cuando se combina con NVIDIA NVLink de quinta generación, ofrece un rendimiento de inferencia LLM en tiempo real 30 veces más rápido para modelos de lenguaje de billones de ...
Precisiones compatibles con Tensor CoreFP64, TF32, BF16, FP16, FP8, INT8, FP6, FP4FP64, TF32, BF16, FP16, FP8, INT8 Precisiones compatibles con CUDA® CoreFP64, FP32, FP16, BF16FP64, FP32, FP16, BF16, INT8 *Las especificaciones preliminares pueden estar sujetas a cambi...
In comparison, for AI training performance, the B200 offers up to 2.5x higher FP8 throughput per GPU over the previous Hopper generation. But its real strength lies in inference – the new FP6 numeric format effectively doubles throughput over FP16, enabling up to 30x higher performance for l...
For future iterations, the number of bits used for models will decrease substantially, as FP4 support comes with the next-generation NVIDIA B100 Blackwell architecture. It is also worth mentioning that for some applications, PTQ may be sufficient while other applications might require q...
It is difficult to accurately compute the differences between the Blackwell chips and existing GPUs because they are no longer benchmarking with the same measurements, as AI uses FP8 and now FP4, which exaggerates the performance numbers compared to traditional FP32-bit computations in graphics. Be...
the LLM will demonstrate some level of decrease in accuracy. Though this is out of scope, it should be mentioned that to overcome any performance decrease you can look into quantization aware training, or train with FP8 or FP4 using an NVIDIA transformer engine along with newer NVIDIA H100 and...
Simple samples for TensorRT programming. Contribute to NVIDIA/trt-samples-for-hackathon-cn development by creating an account on GitHub.
GB200 NVL72 apresenta recursos de ponta e um Transformer Engine de segunda geração que permite IA FP4 e, quando acoplado ao NVIDIA NVLink de quinta geração, oferece desempenho de inferência LLM em tempo real 30 vezes mais rápido para modelos de linguagem de trilhões de parâm...
de Núcleos Tensor, que oferece avanços inovadores para IA generativa, análise de dados e HPC. Os Núcleos Tensor de quinta geração introduzem novas precisões de microescalonamento (MX) FP4 junto com suporte para todas as precisões da comunidade, como MXFP8, MXFP6 e MXINT8....