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NVIDIA B300 benchmarks

288GB

HBM3e Memory Per GPU

8TB/s

Memory Bandwidth

15 PFLOPS

Dense FP4 (per GPU)

1.5×

vs. B200 NVL72

GPU rig

Meet the NVIDIA B300: Blackwell Ultra Built for What Comes After "Big"

Some AI workloads don't just want more compute — they need more room to think. That's the gap the NVIDIA B300 Blackwell GPU was designed to close. Where earlier Blackwell parts start running out of headroom on the largest mixture-of-experts and dense transformer models, the B300 pushes memory capacity and bandwidth further, giving research teams and enterprises the ability to train and serve models that simply wouldn't fit before.

Cyfuture AI now offers the NVIDIA B300 GPU cloud as part of its GPU as a Service lineup, so you can access this hardware the way it should be consumed — on demand, without a multi-month procurement cycle, and without committing capital to a card that depreciates the moment it ships. Whether your team is fine-tuning a mid-size model this week or standing up a multi-node cluster for a quarter-long pre-training run, our AI infrastructure is built to flex with you, backed by real uptime commitments and a support team that answers the phone.

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GPU Cloud Today

Avoid procurement delays and secure high-performance B300 GPU Cloud resources for AI training, inference, and large-scale enterprise workloads.

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Flexible NVIDIA B300 Cloud Pricing,Built Around Your Workload

We're not going to publish a number here and ask you to trust it applies to your situation — B300 GPU hourly pricing depends on configuration, region, commitment length, and current allocation, and quoting a flat figure would do you a disservice. What we can tell you is how the pricing structure works, so you know what to expect before you talk to us:

Pay-as-you-go / on-demand access — ideal if you're testing, benchmarking, or running short-lived jobs and want to avoid any commitment.
Monthly plans — for teams running sustained workloads who want a predictable, lower effective rate than pure on-demand.
6-month and annual reserved terms — the most cost-efficient way to hold B300 GPU on rent for production training pipelines or ongoing inference serving, with meaningfully reduced hourly-equivalent rates the longer you commit.
Custom multi-GPU and cluster pricing — for HGX B300 8-way nodes or larger rack-scale deployments, our team builds a quote around your exact topology, networking, and duration needs.
Configuration GPU Count Ideal For Billing Options
Single B300 instance 1× B300 Fine-tuning, experimentation, smaller inference jobs Hourly, monthly
Multi-GPU B300 node 2×–4× B300 Mid-scale training, batch inference at higher throughput Hourly, monthly, 6-month
HGX B300 (8-way) 8× B300 Large-scale pre-training, trillion-parameter MoE fine-tuning Monthly, 6-month, annual
Reserved cluster Custom (8–256+) Sustained enterprise production workloads Annual / custom contract

B300 GPU - Technical Specifications

Architecture

  • GPU generation: NVIDIA Blackwell Ultra
  • Tensor Core generation: 5th Gen (Ultra), enhanced FP4 Transformer Engine
  • CUDA cores: 20,480+
  • Form factor: SXM / HGX baseboard, data-center liquid-cooled deployment

Choosing Between the B300, B200, and H100

With three NVIDIA GPU generations now in active use across the industry, the question we hear most often isn't "which GPU is fastest" - it's "which one is right for what I'm actually building." Each of these three has a real place in production today, and the honest answer depends on model size, budget, and how much memory headroom your workload genuinely needs. Here's how the NVIDIA B300 Blackwell GPU stacks up against the B200 and the H100.

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)
Typical Best Fit Proven, cost-efficient production workloads Large model training and inference at strong value Frontier-scale, memory-bound, and long-context workloads

B300 Performance at a Glance

A few of the headline jumps, expressed simply — useful if you just want the gist before reading the full breakdown below.

1.5×

more GPU memory vs. B200

~1.7×

FP8 compute vs. B200

3.6×

more GPU memory vs. H100

~3.75×

FP8 compute vs. H100

Use Cases of NVIDIA B300 Cloud GPU

Not every AI workload needs this much GPU. Here's an honest look at where renting a B300 GPU server makes a real difference rather than just adding cost:

Training Frontier-Scale Language Models

When you're building or fine-tuning models north of 200 billion parameters - dense or mixture-of-experts - the B300's memory ceiling gives your team room to work without constantly re-engineering around tensor-parallel splits.

Serving Long-Context, Reasoning-Heavy Applications

Agentic workflows, multi-turn assistants, and retrieval-augmented generation systems that need to hold hundreds of thousands (or millions) of tokens of context lean heavily on memory bandwidth — an area where this GPU is purpose-built to perform.

High-Volume Production Inference

For enterprises serving large language models at real production scale, the compute density of Blackwell Ultra helps bring cost-per-token down meaningfully compared to older-generation Hopper hardware.

Multi-Modal and Generative Media Pipelines

Video generation, 3D content pipelines, and models that fuse text, image, and audio inputs all benefit from the extra memory headroom, letting you push resolution and batch sizes further before hitting a hardware wall.

Scientific and Research Computing

Drug discovery, protein structure prediction, climate simulation, and novel architecture research all lean on the same resource: memory capacity paired with sustained compute throughput.

Rack-Scale Enterprise Deployments

For organizations running continuous, large-scale AI operations, the B300 slots into multi-node HGX and rack-scale configurations designed for sustained, high-utilization workloads.

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.

rent-nvidia-b300-gpu

Why Businesses Choose Cyfuture AI to Rent B300 GPU Capacity

Owning a fleet of B300 GPUs outright means absorbing hardware cost, networking build-out, liquid-cooling infrastructure, and depreciation - all before a single training job runs. Renting through Cyfuture AI's B300 GPU cloud sidesteps that entirely. You get access to Blackwell Ultra-class compute the same week you need it, scaled to exactly the footprint your project calls for.

A few reasons teams pick us over rolling their own hardware or shopping around endlessly for NVIDIA B300 cloud pricing elsewhere:

No inflated markup, no vague quotes — we walk you through what drives your specific rate instead of hiding behind a generic number.
Scale up or down without re-architecting — start with a single instance, move to a full HGX B300 node, or reserve a dedicated cluster, all on the same platform.
Liquid-cooled, data-center-grade deployment — the infrastructure this GPU actually requires to run at sustained load, already built and validated.
24/7 human support — when a training run stalls at an inconvenient hour, you're talking to an engineer, not a ticket queue.
India-hosted infrastructure options - useful for teams with data residency or latency requirements within the region.

If you've been comparing where to rent B300 GPU server capacity, it's worth getting an actual conversation and a real quote from our team before locking into a provider based on a headline number alone.

Pre-Reserve NVIDIA
Blackwell B300 GPUs

Get priority access to enterprise-grade NVIDIA B300 GPU infrastructure and be ready to launch your AI projects the moment capacity becomes available.

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H200 GPUs

Key Benefits of B300 GPU

Memory Headroom
Memory Headroom Most GPUs Can't Match

With 288GB of HBM3e per GPU and bandwidth reaching up to 8 TB/s, the B300 lets you keep larger models, longer context windows, and bigger batch sizes in memory at once - cutting down on the workarounds smaller-memory GPUs force on your team.

Near-Linear Multi-GPU Scaling
Near-Linear Multi-GPU Scaling

NVLink Switch System connectivity across 8-GPU HGX B300 nodes keeps distributed training frameworks like FSDP, DeepSpeed ZeRO-3, and Megatron scaling efficiently as you add GPUs, instead of hitting diminishing returns past two or three nodes.

Deployment That Matches Your Growth Curve
Deployment That Matches Your Growth Curve

Start with a single B300 GPU on rent for a proof of concept, scale into a multi-GPU node as your model grows, or move to a reserved rack-scale cluster once your workload is production-stable — all under one account, one support relationship, and one billing structure.

Ready to Put Blackwell Ultra to Work?
Ready to Put Blackwell Ultra to Work?

Access the NVIDIA B300 GPU cloud on your terms - on-demand for agility, reserved for predictability, and always with a real person on the other end when you need one.

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FAQs: NVIDIA B300 GPU

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.

The NVIDIA B300 is a Blackwell Ultra-generation GPU built for the largest AI training and inference workloads in production today. It combines 288GB of HBM3e memory with a 5th-generation Tensor Core design, making it a fit for trillion-parameter model training and ultra-long-context inference.

The B300 pushes memory capacity roughly 50% higher than the prior Blackwell generation, adds meaningfully more bandwidth, and refines the Transformer Engine for stronger FP4 throughput — all aimed at workloads that were previously memory-constrained.

Rates depend on GPU count, commitment length, and configuration, so we don't publish a single fixed number here. Reach out to our team for current NVIDIA B300 GPU price details and a quote scoped to your actual workload.

Both. Cyfuture AI supports single-instance rentals for smaller projects and testing, alongside full HGX B300 8-way nodes and larger clusters for production-scale training and inference.

It handles both well. Training benefits from the memory capacity for larger batch sizes and bigger models; inference benefits from the FP4 Transformer Engine and bandwidth when serving very large or long-context models in production.

No — on-demand, hourly access is available without any commitment. Monthly and longer reserved terms exist purely as a way to lower your effective rate if your workload is ongoing.

PyTorch, TensorFlow, and JAX are all supported, alongside NVIDIA's CUDA, cuDNN, and TensorRT stack tuned for Blackwell Ultra, plus Blackwell-optimized NeMo Framework containers for large-scale training.

Straightforward conversations about pricing instead of hidden fees, flexible scaling from single-GPU to full cluster, liquid-cooled infrastructure built for this hardware's actual power draw, and 24/7 support from people who understand the workloads running on it.

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