Interconnect: NVLink 5
Software Stack: CUDA, cuDNN, TensorRT, NVIDIA AI Enterprise
Organizations that typically deploy B100 GPUs include:
The GPU is particularly attractive for buyers requiring cutting-edge AI training hardware with high cluster scalability.
The B100 is deployed primarily through high-end AI training systems and large cloud infrastructure platforms. Availability may initially be limited due to the complexity of advanced semiconductor manufacturing and the strong demand for Blackwell-based GPUs.
Because of its positioning in large-scale AI infrastructure, the B100 is typically deployed in multi-GPU server nodes and hyperscale AI clusters.
Increasing adoption of Blackwell architecture for large-scale generative AI deployments.
The NVIDIA B100 is a high-performance data center GPU built on the Blackwell architecture, designed to support next-generation AI training and inference workloads. It represents a major evolution beyond the Hopper architecture, delivering substantial improvements in tensor performance, memory bandwidth, and energy efficiency.
Blackwell GPUs introduce fifth-generation Tensor Cores, optimized for modern deep learning workloads including large language models, generative AI systems, and multi-modal models. These improvements enable higher throughput for low-precision AI operations such as FP8 and FP4, which are increasingly used in large-scale training pipelines.
The B100 is typically deployed in large AI training clusters that require high compute density and fast GPU-to-GPU communication. Through technologies such as NVLink 5 interconnects and advanced memory subsystems, the architecture enables efficient scaling across multi-GPU and multi-node training environments.
| Specification | Value |
|---|---|
| Architecture | NVIDIA Blackwell |
| CUDA Cores | ~15,000+ (estimated configuration) |
| Tensor Cores | 5th Gen Tensor Cores |
| Memory | 192 GB HBM3e |
| Memory Bandwidth | ~8 TB/s |
| Interconnect | NVLink 5 |
| Form Factor | SXM |
| Max TGP | ~700-1000 W |
| Precision Support | FP64, TF32, FP32, FP16, BF16, FP8, FP4 |
| Typical AI Compute | ~10-15 PFLOPS (FP4 Tensor, estimated) |
| Process Node | TSMC 4NP |
| Transistor Count | ~200+ Billion |
| MIG Support | Supported |
| NVLink (Peer) | ~1.8 TB/s bidirectional |
Compared with Hopper GPUs such as the H100 and H200, the B100 delivers significantly improved tensor throughput and memory capacity for large-scale AI workloads.
Compared with Hopper GPUs such as the H100 and H200, the B100 delivers significantly improved tensor throughput and memory capacity for large-scale AI workloads.
Comparable NVIDIA GPUs:
Competitor Accelerators:
These accelerators compete in the market for large-scale AI training infrastructure.
Related NVIDIA GPUs:
Competing AI Hardware:
The NVIDIA B100 is a high-performance data center GPU designed for large-scale AI training and inference workloads. Built on the Blackwell architecture, it introduces major improvements in tensor performance, memory bandwidth, and multi-GPU interconnect capabilities.
With support for advanced precision formats such as FP8 and FP4, the B100 enables highly efficient training of modern generative AI models and large language models. As part of NVIDIA’s Blackwell GPU lineup, it serves as a key accelerator for next-generation AI infrastructure and hyperscale training clusters.
The next GPU to add should be NVIDIA GB200.
This is important because:
Below is the page written in the same structure as your other GPU pages.
