Benchmarks comparing inference performance of the NVIDIA GH200 Grace Hopper Superchip, enhanced by ZeRO-Inference, to NVIDIA H100 and A100 Tensor Core GPUs.
Benchmarks on NVIDIA’s Transformer Engine, which boosts FP8 performance by an impressive 60% on GPT3-style model testing on NVIDIA H100 Tensor Core GPUs.
GPU benchmarks on Lambda’s offering of the NVIDIA H100 SXM5 vs the NVIDIA A100 SXM4 using DeepChat’s 3-step training example.
How to use FlashAttention-2 on Lambda Cloud, including H100 vs A100 benchmark results for training GPT-3-style models using the new model.
RTX 4090 vs RTX 3090 benchmarks to assess deep learning training performance, including training throughput/$, throughput/watt, and multi-GPU scaling.
Lambda presents stable diffusion benchmarks with different GPUs including A100, RTX 3090, RTX A6000, RTX 3080, and RTX 8000, as well as various CPUs.
This article discusses the performance and scalability of H100 GPUs and the whys for upgrading your ML infrastructure with the H100 release from NVIDIA.
GPU benchmarks on NVIDIA A40 GPUs with 48 GB of GDDR6 VRAM, including performance comparisons to the NVIDIA V100, RTX 8000, RTX 6000, and RTX 5000.
This post discusses the Total Cost of Ownership (TCO) for a variety of Lambda A100 servers and clusters. We calculate the TCO for individual Hyperplane-A100 servers, compare the cost with renting a AWS p4d.24xlarge instance, and walk through the cost of building and operating A100 clusters.
PyTorch benchmarks of the RTX A6000 and RTX 3090 for convnets and language models - both 32-bit and mix precision performance.
PyTorch & TensorFlow benchmarks of the Tesla A100 and V100 for convnets and language models - both both 32-bit and mix precision performance.
PyTorch and TensorFlow training speeds on models like ResNet-50, SSD, and Tacotron 2. Compare performance of the RTX 3090, 3080, A100, V100, and A6000.
Benchmarks for ResNet-152, Inception v3, Inception v4, VGG-16, AlexNet, SSD300, and ResNet-50 using the NVIDIA A100 GPU and DGX A100 server.
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