GPU
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NVIDIA B200

Blackwell-based flagship data center GPU for generative AI, LLM training, and high-throughput inference.

Release

2024

GPU Class

Data Center / AI Accelerator

Architecture

Blackwell

PRICE SNAPSHOT

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On-premise Module

$30k-$50k

Turnkey System

~$500k+

Cloud Pricing
(per GPU/hr)

~$2.49 – $8+/hr

chip identity

nvdia-gpu-b200

On-premise Module

B200

GPU Class

Data Center / AI Accelerator

Release

2024

Architecture

Blackwell

Target Workload

  • LLM Training (1T+ parameters)
  • High-throughput Inference
  • Multi-modal Generative AI

Compatible Platforms

  • DGX B200 systems
  • HGX B200 baseboards
  • Custom AI clusters via OEM

Ideal Buyer Profile

Enterprises and research organizations requiring:

  • Massive memory and compute for LLM training
  • Unified training + inference hardware
  • Dense GPU clusters with NVLink fabric

Typical adopters include AI cloud platforms, hyperscalers, and institutions developing custom large AI models.Availability Notes

Availability Notes

The B200 launched into production as NVIDIA’s flagship Blackwell GPU for data centers; pricing tends to reflect its premium positioning. Early availability was paced due to ramp‑up cycles typical of advanced semiconductor yields.

Recent Developments

  • The B200 has been highlighted in technical comparisons for its architectural improvements over H200 and H100.
  • Software ecosystem optimizations continue to evolve around NVIDIA AI Enterprise, CUDA, and optimized frameworks that fully utilize Blackwell features.

overview

The NVIDIA B200 is NVIDIA’s current flagship data‑center GPU built on the next‑generation Blackwell architecture, designed to dramatically advance AI training and inference performance. The B200 targets hyperscale generative AI workloads, including large language models (LLMs), multi‑modality, and high‑throughput inference serving.

Key architectural innovations include fifth‑generation Tensor Cores, expanded ultra‑high bandwidth memory, and enhanced interconnect fabric for multi‑GPU scaling.

Key specifications

Specification B200 GPU
Architecture NVIDIA Blackwell
CUDA Cores ~16,896 (derived relative to H100 comparisons)
Tensor Cores ~528
Memory 192 GB HBM3e
Memory Bandwidth ~8 TB/s
Interconnect NVLink 5 (multi-GPU)
Form Factor SXM
Max TGP ~1000 W
Precision Support FP64, TF32, FP16/FP8/FP4
Typical AI Compute ~20 PFLOPS (FP4)
Process Node TSMC 4NP
Transistor Count ~208 billion
MIG Support Supported
NVLink (peer) 1.8 TB/s bidirectional (est.)

Performance Summary

  • AI/ML Throughput: Estimated ~20 PFLOPS of FP4 tensor performance per GPU.
  • Tensor Compute: Strong performance across FP8/INT8/FP4 workloads for training and inference; fifth‑gen Tensor Cores improve efficiency.
  • Memory Bandwidth: Ultra‑high HBM3e bandwidth (~8 TB/s) supports large context windows and batch sizes without frequent host memory access.
  • Multi‑GPU Scaling: Enhanced NVLink interconnect enables high bandwidth between GPUs for large‑model training.
  • Compared to prior‑generation Hopper GPUs (e.g., H200/H100), the B200 delivers significantly higher memory capacity and tensor performance, especially at low precision.

primary use case

  • Trillion‑parameter LLM training and fine‑tuning
  • High‑throughput real‑time inference for chat, search, and recommendation systems
  • Generative AI workloads with multi‑modal data
  • Hyperscale AI clusters where memory and interconnect yield efficiency gains
  • The architecture’s focus on both compute and memory bandwidth makes it suitable for mixed precision workflows common in modern transformer‑based models.

Alternatives & Upgrade Path

Comparable NVIDIA GPUs:

  • H200 / H100: Previous generation high‑performance GPUs with lower memory and tensor throughput.
  • B100: Earlier Blackwell variant with reduced memory and compute relative to B200.

Competitor GPUs:

  • Accelerators from AMD and custom AI silicon focus on domain‑specific performance but typically trail the B200 in overall AI training/inference ecosystem support and software maturity.

Related Chips & Providers

Related NVIDIA GPUs:

  • B100 (Blackwell lower tier)
  • HGX B200 multi‑GPU boards
  • GB200 “superchip” variants integrating multiple Blackwell dies (distinct architecture)

Complementary Silicon:

  • Grace CPUs for accelerated CPU‑GPU pairing in advanced AI systems.

SUMMARY

The NVIDIA B200 represents the current pinnacle of NVIDIA’s datacenter GPU lineup, combining high‑capacity memory, leading tensor performance, and advanced interconnect for scalable AI workloads. It is engineered to accelerate next‑generation generative AI models, both in training and inference, and serves as a strategic backbone for enterprise and cloud AI infrastructure.

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