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At a Glance: NVIDIA B200 GPU Performance

15X

Faster Inference

vs. DGX H100

3X

Training Performance

Accelerated AI Model Training

1,440 GB

Total HBM3e Memory

Ultra-high GPU Memory Capacity

144 PFLOPS

Tensor Performance

FP4 Tensor Core Computing

Meet the NVIDIA B200: The GPU That Made Blackwell Real

Before there was a B300, there was the B200 - and it's the GPU that actually moved the industry off Hopper. It doubled the memory of the H100, brought native FP4 support to production for the first time, and gave teams a realistic path to training and serving models that simply didn't fit on a single H100 or H200. For most organizations, the B200 isn't a stepping stone to something bigger; it's the right amount of GPU for the workload in front of them.

Cyfuture AI offers the NVIDIA B200 GPU cloud as part of its GPU as a Service lineup, so you can get on Blackwell this week instead of waiting out a hardware procurement cycle or tying up capital in cards that lose value the moment they ship. Whether you're fine-tuning a 70B-parameter model or standing up an 8-GPU HGX node for a production inference service, our AI infrastructure is built to scale with the job, backed by real uptime commitments and a support team you can actually reach.

GPU rig
gb200-superchip-ai

How B200 GPU Cloud Pricing Works

Rather than posting one number and hoping it matches your situation, here's the logic behind our B200 rates - what actually moves the price up or down, and which billing model tends to fit which kind of workload:

On-demand, billed hourly suited to short experiments, benchmarking runs, and anything you don't want to lock into a plan.
Monthly billing for workloads that run continuously enough that a flat monthly rate beats hourly pricing.
6-month and annual commitments the lowest hourly-equivalent cost, aimed at production training pipelines and always-on inference services.
Custom cluster agreements for HGX B200 8-way deployments or larger, priced against your networking, topology, and duration needs.

NVIDIA B200
GPU Specifications (Per GPU) :

Deployment GPUs Fits Best Billing
Single B200 1 Fine-tuning, prototyping, dev/test workloads Hourly, monthly
B200 multi-GPU node 2-4 Higher-throughput training and batch inference Hourly, monthly, 6-month
HGX B200 node 8 Production-scale training, 100B+ parameter fine-tuning Monthly, 6-month, annual
Dedicated cluster Custom (8-256+) Always-on enterprise production systems Annual / custom

Get a B200 Quote
for Your Workload

Tell us what you're training or serving and we'll size the right B200 configuration and billing term - no generic package required.

Request Pricing

B200 GPU - Specs at a Glance

Architecture

  • Core architecture: Built on the NVIDIA Blackwell architecture
  • Tensor Cores: 5th-generation Tensor Cores with native FP4 support
  • CUDA cores: 18,000+ CUDA cores per GPU
  • Form factor: SXM / HGX form factor, air- or liquid-cooled depending on deployment density

B200 vs H100 vs B300: Picking the Right Blackwell-Class GPU

The honest way to think about these three isn't a ranking - it's a fit question. The H100 is still a perfectly reasonable choice for workloads that never stretched its memory in the first place. The B300 exists for teams that are genuinely memory-bound at the frontier. Everything in between - which is most production AI work today - is where the B200 lives.

Attribute NVIDIA H100 NVIDIA B200 NVIDIA B300
Architecture Hopper Blackwell Blackwell Ultra
GPU Memory 80GB HBM3 192GB HBM3e 288GB HBM3e
Memory Bandwidth ~3.35 TB/s ~8 TB/s up to 8 TB/s (higher sustained efficiency)
Tensor Core Generation 4th Gen 5th Gen 5th Gen (Ultra), enhanced FP4 Transformer Engine
FP8 Performance (dense, per GPU) ~2,000 TFLOPS ~4,500 TFLOPS ~7,500 TFLOPS
FP4 Support Not supported natively Supported Supported, with enhanced throughput
NVLink Bandwidth 900 GB/s 1.8 TB/s 1.8 TB/s (optimized for larger clusters)
Best Fit Cost-efficient, proven production workloads Large-scale training and inference at strong value Frontier-scale, memory-bound, long-context workloads

The Numbers That Matter Most

If you're skimming, these are the four figures worth remembering when comparing B200 to H100:

2.4x

GPU memory vs. H100

~2.3x

FP8 throughput vs. H100

2x

NVLink bandwidth vs. H100

4x

faster LLM training vs. H100

Real-World Scenarios Where B200 Earns Its Keep

Not every job justifies the largest GPU available. These are the situations where a B200 rental is the clearly right call:

Fine-Tuning Mid-to-Large Language Models

Somewhere between 7B and 200B parameters, an 80GB card starts forcing awkward parallelism decisions. The B200's 192GB gives that range room to breathe without a rewrite of your training setup.

Serving Production LLM Traffic

Native FP4 support plus 8 TB/s of bandwidth brings cost-per-token down noticeably versus Hopper-based serving, without needing the extra memory ceiling of a B300.

Image, Video, and Audio Generation

Generative media pipelines that fuse multiple modalities benefit directly from the memory jump over H100 - bigger batches and higher resolutions before you hit a hardware ceiling.

Context-Heavy Assistants and RAG Systems

Applications holding large retrieved context or long conversation history lean on exactly the memory and bandwidth profile the B200 was built to deliver.

Simulation and Scientific Workloads

Research work that needs sustained FP64/FP32 throughput alongside generous memory - protein modeling, physical simulation, and similar - runs comfortably on B200 infrastructure.

Steady-State Enterprise AI Operations

Teams that need reliable, always-on capacity but haven't hit the ceiling that justifies rack-scale B300 clusters get a proven, well-supported platform in HGX B200 nodes.

Voices of Innovation: How We're Shaping AI Together

We're not just delivering AI infrastructure-we're your trusted AI solutions provider, empowering enterprises to lead the AI revolution and build the future with breakthrough generative AI models.

KPMG optimized workflows, automating tasks and boosting efficiency across teams.

H&R Block unlocked organizational knowledge, empowering faster, more accurate client responses.

TomTom AI has introduced an AI assistant for in-car digital cockpits while simplifying its mapmaking with AI.

Reserve NVIDIA B200 Capacity Ahead of Demand

Lock in priority access to B200 GPU infrastructure so your team can start the moment capacity opens up, instead of waiting in a queue.

Reserve Capacity
H200 GPUs
b200-gpu-book

The Cyfuture AI Advantage for B200 Rentals

Buying B200 hardware outright means absorbing its full cost, plus networking, cooling, and the fact that it starts depreciating the day it arrives. Renting through Cyfuture AI removes that calculation - you get Blackwell-class compute sized to the job, active within days rather than months.

What teams tell us actually matters when they compare us to rolling their own hardware or shopping around for B200 pricing elsewhere:

Transparent rate breakdowns we explain what drives your price instead of quoting a black-box number.
Frictionless scaling go from a single card to a full HGX node to a dedicated cluster without switching platforms.
Liquid-cooled, data-center-grade deployment the infrastructure built and validated for sustained Blackwell workloads.
Support from actual engineers, around the clock not a ticket queue that gets to you tomorrow.
India-hosted options for teams with data residency or latency constraints in the region.

If you're weighing multiple B200 GPU cloud providers, it's worth getting a real, workload-specific quote from us before deciding based on a headline hourly rate alone.

The Cyfuture AI Advantage for NVIDIA B200 AI Infrastructure

Memory Headroom That Changes What You Can Build

192GB of HBM3e and up to 8 TB/s of bandwidth mean larger models, longer context windows, and bigger batches stay in memory at once - fewer workarounds than an 80GB H100 forces on your team.

Scaling That Doesn't Fall Apart Past Two Nodes

NVLink Switch connectivity across an 8-GPU HGX B200 node keeps frameworks like FSDP, DeepSpeed ZeRO-3, and Megatron scaling close to linearly as GPU count grows.

A Path That Grows With Your Workload

Start on a single B200 for a proof of concept, move to a multi-GPU node as the model grows, and step up to a reserved HGX cluster once the workload is production-stable - one account and one billing relationship throughout.

Support That Doesn't Disappear After Deployment

Whether you're running on-demand for a short project or reserved capacity for a year-long production system, the same support team is reachable when something needs attention.

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NVIDIA B200 GPU Rental

The power of AI, backed by human support

At Cyfuture AI, we combine advanced technology with genuine care. Our expert team is always ready to guide you through setup, resolve your queries, and ensure your experience with Cyfuture AI remains seamless. Reach out through our live chat or drop us an email at [email protected] - help is only a click away.

It's NVIDIA's Blackwell-generation data center GPU, built with 192GB of HBM3e memory, 5th-generation Tensor Cores, and native FP4 precision support - aimed at large-scale AI training and high-volume inference.

More than double the memory, more than double the bandwidth, and FP4 support the H100 simply doesn't have at the hardware level. Combined, that typically shows up as roughly 4x faster training and considerably cheaper inference at scale.

It depends on GPU count, term length, and configuration, so we don't post one flat figure. Reach out and our team will scope current NVIDIA B200 pricing against your specific workload.

You can start with a single B200 instance for testing or a smaller project, and move up to an HGX B200 8-way node or a larger custom cluster whenever the workload calls for it.

Both, genuinely. The memory capacity helps with larger training batches and bigger models, while native FP4 support and high bandwidth make it efficient for serving large models in production.

No. Hourly, on-demand access is available with zero commitment. Monthly and reserved terms are there only to lower your rate if the workload runs continuously.

PyTorch, TensorFlow, and JAX are all supported, along with NVIDIA's CUDA, cuDNN, and TensorRT stack built for Blackwell, plus Blackwell-optimized NeMo Framework containers.

Clear pricing conversations instead of hidden fees, easy scaling from a single GPU to a full cluster, infrastructure already built for Blackwell's power and cooling needs, and 24/7 support from people who understand the workloads running on it.

Get Started With NVIDIA B200 GPU Cloud

Book NVIDIA B200 GPU capacity with the deployment size, billing term, and support level your project actually needs.

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