Best Cloud GPU Providers with NVIDIA B300

What the NVIDIA B300 actually is

The B300 is NVIDIA’s Blackwell Ultra data-center GPU, the mid-cycle refresh that sits above the original B200 in the same Blackwell generation. It is built for the era of large-scale reasoning models and trillion-parameter inference, and it is the GPU you are filtering for in the comparison above. When you rent a B300 instance, you are renting one of the most capable single accelerators currently available for production AI, so it pays to understand what that hardware buys you before you commit to an hourly rate.

The headline figures that matter for renters are concrete and verifiable:

• 288 GB of HBM3e memory per GPU, delivered through 12-high stacks — a roughly 50% capacity increase over the 192 GB B200.
• Around 8 TB/s of memory bandwidth, which keeps the very large on-package memory fed during attention-heavy and memory-bound workloads.
• Roughly 15 petaFLOPS of dense FP4 compute per GPU, driven by 640 fifth-generation Tensor Cores and 20,480 CUDA cores.
• Native support for low-precision AI formats including FP4, FP8, BF16 and FP16, with the second-generation Transformer Engine handling the narrow precisions that modern inference relies on.
• A high power envelope of around 1,400 W per GPU, which is why B300 deployments are almost always liquid-cooled rack systems rather than loose PCIe cards.

Interconnect and multi-GPU scaling

A single B300 is powerful, but the design assumption is that you rarely use just one. Each GPU carries fifth-generation NVLink with about 1.8 TB/s of total bandwidth, and in the GB300 NVL72 rack-scale form, 72 Blackwell Ultra GPUs are paired with 36 Grace CPUs and stitched together so their HBM3e is exposed as one coherent memory pool over the NVLink fabric. For renters, the practical implications are:

• Multi-GPU and multi-node B300 allocations behave less like a cluster of separate cards and more like a single very large accelerator, which is what makes serving and training huge models feasible.
• If your model spans several GPUs, the interconnect — not raw per-GPU FLOPS — often determines real throughput, so it is worth checking whether a listing offers true NVLink-connected GPUs or merely several PCIe cards in one box.
• The 288 GB per GPU means many models that previously needed sharding across multiple cards can now fit on fewer GPUs, lowering communication overhead and sometimes total cost.

Which workloads the B300 genuinely fits

The B300 is purpose-built for the heaviest end of modern AI, and its strengths and mismatches are fairly clear-cut.

Where it excels

• Large-model and reasoning-model inference: the large HBM3e capacity and FP4 throughput are specifically tuned for long-context, high-concurrency serving of frontier and reasoning models, where attention performance and memory headroom dominate.
• Training and fine-tuning of very large models: trillion-parameter and mixture-of-experts training benefit from the memory capacity, bandwidth and NVLink scaling.
• High-throughput batch inference: when you can batch requests aggressively, FP4/FP8 execution lets a B300 push enormous token volumes per dollar of compute time.

Where it is overkill

• Small or mid-sized models that fit comfortably in 24–80 GB of VRAM will not come close to saturating a B300, and you would be paying premium rates for capacity you cannot use.
• Single-stream, low-batch real-time inference of a modest model is usually better served by smaller, cheaper accelerators.
• Rendering, simulation and classic HPC can run on a B300, but unless the job is genuinely memory-hungry or precision-flexible, more modest cards deliver better value.

Rental context: cost, availability and scarcity

Because the B300 is current-generation flagship silicon, it sits firmly at the top of the cloud GPU cost spectrum — expect it to be among the priciest per-hour options in the list above, well clear of last-generation Hopper-class cards. Exact rates move constantly and differ between providers, so treat the live comparison above as the source of truth rather than any figure you read in prose.

A few rental realities are worth planning around:

• Scarcity: newly released top-tier GPUs are frequently capacity-constrained. On-demand availability can be limited, and you may encounter reservation, minimum-commitment or waitlist requirements rather than instant click-to-launch access.
• Spot vs on-demand: interruptible or spot B300 capacity, where offered, can cut costs meaningfully, but it suits checkpointed training and fault-tolerant batch jobs far more than latency-sensitive production serving.
• Whole-rack vs fractional: some offers are rack-scale GB300 systems aimed at large clusters, while others expose individual GPUs — match the granularity to your actual workload so you are not renting 72 GPUs to serve one model.
• Surrounding spec: CPU, system memory, local NVMe and network bandwidth vary by provider and can bottleneck an otherwise fast GPU, so compare the full instance rather than the GPU label alone.

Frequently asked questions

How much memory does a B300 have, and why does it matter for renting?

Each B300 provides 288 GB of HBM3e at roughly 8 TB/s of bandwidth. That large, fast memory pool lets you serve longer contexts and bigger models on fewer GPUs, which can directly lower how many instances — and therefore how many hours — you need to pay for.

Is the B300 worth renting over a B200 or an H100?

It depends on scale. For frontier-scale training and high-concurrency reasoning inference, the B300’s extra memory, FP4 throughput and attention performance justify the premium. For smaller models or modest serving, a B200 or a Hopper-class H100 is usually more cost-effective, so weigh the per-hour rates in the table against your actual model size.

Can I rent a single B300, or only full systems?

Both patterns exist in the market. Some providers expose individual NVLink-connected GPUs while others rent whole GB300 NVL72 racks for large clusters. Check the listing in the comparison above to see the granularity, and confirm whether multiple GPUs are truly NVLink-linked or just co-located on PCIe.

Is B300 capacity readily available on demand?

Not always. As current-generation flagship hardware it is often in high demand, so on-demand slots can be scarce and some access is reservation-based. If your workload can tolerate interruption, spot or preemptible capacity can ease both availability and cost.