The NVIDIA B200 is a GPU based on the Blackwell architecture, announced in March 2024 and shipping in late 2024. It succeeds the Hopper H100 and is designed specifically for large-scale AI training and inference workloads. The B200 is built on a custom TSMC 4NP process and integrates 208 billion transistors across two reticle-limited dies connected via a high-speed NVLink-HBI interface (10 TB/s).
Technically, the B200 introduces several key innovations. It supports a new FP4 (4-bit floating point) precision, delivering up to 20.2 PetaFLOPS for sparse inference, which is roughly 4x the FP8 throughput of the H100. Memory is a critical upgrade: the B200 comes with 384 GB of HBM3e memory (up from 80 GB HBM3 on H100) and a memory bandwidth of 8 TB/s (up from 3.35 TB/s). This enables loading significantly larger models — such as a 70B-parameter model entirely in GPU memory without model parallelism — drastically reducing inference latency. The B200 also includes a second-generation Transformer Engine with micro-tensor scaling (per-tensor and per-group scaling) for improved accuracy at low precision, and a dedicated dequantization unit.
Why it matters: The B200 addresses the memory wall that constrained previous GPUs. For inference, large models like Llama 3.1 405B previously required multiple H100s, but the B200’s 384 GB capacity allows single-GPU inference for many 70B–100B models, cutting cost and latency. For training, the B200 supports FP8 and FP4 training, though FP4 training remains experimental as of 2026; most production training still uses FP8/BF16. The B200 also introduces NVLink 5.0 (1.8 TB/s per GPU) and NVSwitch 5.0 for scaling to thousands of GPUs.
When used vs alternatives: The B200 is best for inference-heavy deployments (chatbots, code generation, real-time agents) where memory capacity and low-precision throughput matter. For pure training of very large models (e.g., >1T parameters), the B200 is often used in clusters of 8–64 GPUs, but AMD MI300X and Intel Gaudi 3 are cheaper alternatives for training at lower scale. The B200’s high price (~$30k–$40k per GPU) makes it less cost-effective for small-scale projects; cloud instances (e.g., AWS p5e, Azure ND H200) are common.
Common pitfalls: Over-reliance on FP4 for accuracy-critical tasks (e.g., medical or legal) can lead to quality degradation; many teams still use FP8 for safety. The B200’s 700W TDP requires liquid cooling in dense clusters, increasing datacenter costs. Also, software support for Blackwell’s new features (e.g., NVLink 5.0, FP4 kernels) is still maturing in PyTorch and TensorFlow as of early 2026; some HPC workloads see limited benefit.
Current state of the art (2026): The B200 is widely deployed in hyperscaler datacenters. NVIDIA has announced the B200 Ultra (2x B200 dies on a single board) for 2026, and the next-generation Rubin architecture is in development. The B200 remains the de facto standard for premium AI inference, though AMD MI400 and custom ASICs (e.g., Google TPU v6, AWS Trainium 3) are gaining traction.