首发于GPU 切换模式写文章 登录/注册 A100 架构 舞灵 AI算法工程师 目录 收起 性能数据 硬件数据 GPU 架构图 SM 架构图 FP16 Tensor Core 算力推导 FP64 Tensor Core 算力推导 性能数据 硬件数据 制程晶体管裸晶尺寸功耗 7nm N7 54.2 billion 826 mm2 400 W Memory Size L2 Cache Share Memory Size ...
GPU Max Clock rate 1410 MHz (1.41 GHz) Memory Clock rate 1215 Mhz Memory Bus Width 5120-bit L2 Cache Size 41943040 bytes Maximum Texture Dimension Size (x, y, z) 1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384) Maximum Layered 1D Texture Size, (num) layers 1D=(32768),...
BERT Large Inference | NVIDIA TensorRT™(TRT) 7.1 | NVIDIA T4 Tensor Core GPU: TRT 7.1, precision = INT8, batch size = 256 | V100: TRT 7.1, precision = FP16, batch size = 256 | A100 with 1 or 7 MIG instances of 1g.5gb: batch size = 94, precision = INT8 with sparsity....
Change the size of a VM General purpose Compute optimized Memory optimized Storage optimized GPU - accelerated compute Overview NC family ND family Overview NDasrA100_v4 series NDm_A100_v4 series ND series ND series retirement NDv2 series
MULTI-INSTANCE GPU (MIG) An A100 GPU can be partitioned into as many as seven GPU instances, fully isolated at the hardware level with their own high-bandwidth memory, cache, and compute cores. MIG gives developers access to breakthrough acceleration for all their applications, and IT ...
2288H V5配置Tesla A100 40G时,Linux操作系统下执行lspci -vvv -s b9:00.0出现MMIOH资源不足问题,即回显中存在Region 1: Memory at <unassigned> (64-bit, prefetchable),如下图所示。 b9:00.0为Tesla A100 40G在操作系统下的bus总线地址,不同硬件配置下该bus总线地址可能不同。
, name: "/device:GPU:0" device_type: "GPU" memory_limit: 38453856175 locality { bus_id: 6 numa_node: 5 links { } } incarnation: 3682405687960901280 physical_device_desc: "device: 0, name: A100-SXM4-40GB, pci bus id: 0000:cb:00.0, compute capability: 8.0" ...
LambdaLabs 有个很好的 GPU 单机训练性能和成本对比,在此摘录如下。 首先看吞吐量,看起来没有什么违和的,在单卡能放下模型的情况下,确实是 H100 的吞吐量最高,达到 4090 的两倍。看算力和内存也能看出来,H100 的 FP16 算力大约是 4090 的 6 倍,内存带宽是 3.35 ...
2288H V5配置Tesla A100 40G时,Linux操作系统下执行lspci -vvv -s b9:00.0出现MMIOH资源不足问题,即回显中存在Region 1: Memory at <unassigned> (64-bit, prefetchable),如下图所示。 b9:00.0为Tesla A100 40G在操作系统下的bus总线地址,不同硬件配置下该bus总线地址可能不同。
LambdaLabs 有个很好的 GPU 单机训练性能和成本对比,在此摘录如下。 首先看吞吐量,看起来没有什么违和的,在单卡能放下模型的情况下,确实是 H100 的吞吐量最高,达到 4090 的两倍。看算力和内存也能看出来,H100 的 FP16 算力大约是 4090 的 3 倍,内存带宽是 3.35 倍,训练过程中由于 batch size 比较大,大...