The pace of AI infrastructure is driven by one constant: larger models and higher performance demands. Built on the NVIDIA Blackwell Ultra architecture, the NVIDIA B300 exists to solve one problem. Models keep growing, context windows keep stretching and reasoning workloads keep asking for more memory and more bandwidth than the previous generation could give.
If your team has hit memory limits on NVIDIA H100 clusters or you are scoping infrastructure for a generative AI product that needs to scale past a prototype, the NVIDIA B300 is the GPU worth understanding before your next infrastructure decision. Our latest guide covers everything you need to know before running an NVIDIA B300 on demand.
The NVIDIA B300 is NVIDIA's Blackwell Ultra architecture GPU, built for generative AI, reasoning models and large-scale training and inference. A standard NVIDIA B300 server ships with 8x NVIDIA B300 GPUs and the configuration is designed for high-density compute rather than general-purpose workloads.
Three numbers matter most if you are evaluating NVIDIA B300 GPU specs for a generative AI workload: memory, memory bandwidth and interconnect speed. The NVIDIA B300 moves all three forward by a wide enough margin that it changes what you can run on a single GPU, not just how fast you can run it.
Here are the key specifications behind the NVIDIA B300's AI performance.
In terms of the listed specifications, the jump from NVIDIA H100 to NVIDIA B300 is not incremental. A single NVIDIA B300 has close to the same memory as 3.6 NVIDIA H100s combined. That means you can fit a model that used to require 3 NVIDIA H100s onto a single NVIDIA B300, or you can keep your existing model size and get significantly more headroom for context length, batch size or KV cache. Plan against usable capacity rather than the headline figure, since weights, activations and KV cache all draw on the same pool.
The interconnect story matters just as much as the memory story. NVIDIA B300 servers roughly double both GPU-to-GPU and server-to-server interconnect speeds compared to NVIDIA H100 servers. Any workload that spans more than one GPU or more than one server benefits from that doubling directly. You are not just getting bigger, more powerful GPUs. You are getting faster communication between them, which is where a lot of real-world training and inference time actually goes.
NVIDIA B300 servers also ship with considerably more powerful CPUs and RAM than NVIDIA H100 servers, and that comes included in the price. This is easy to overlook, but it changes the cost math. For FP4 and FP8 workloads, the efficiency gain you get is larger than the price increase from NVIDIA H100 to NVIDIA B300, which means your actual cost per unit of performance improves, not just your compute. One caveat worth knowing: NVIDIA Blackwell Ultra trades INT8 and FP64 throughput for FP4 performance, so INT8-quantised inference and FP64 or HPC workloads still run faster on NVIDIA H100.
For running AI inferencing workloads, the story is not just that the NVIDIA B300 computes faster. It is that the 3.6x increase in memory, up to 288 GB per GPU compared to 80 GB on an NVIDIA H100, changes what fits on a single GPU in the first place.
That extra memory gives you two real options. You can run a much larger model on a single NVIDIA B300 instead of splitting it across multiple NVIDIA H100s and taking the performance hit that comes with GPU-to-GPU communication. Or you can keep the same model size and use the extra headroom for longer context windows and higher throughput, measured in tokens per second.
Training is a fundamentally different workload from inference, and the calculus is more straightforward. Inference is about getting the best cost per unit of performance. Training is about having as much memory and compute available as possible because the constraint is usually whether your weights, gradients, optimiser states and activations fit at all, not just how fast a single step runs.
For training, the NVIDIA B300 is close to a default choice over smaller GPUs. The same server-level upgrades that help inference — faster GPU-to-GPU and server-to-server interconnect and more powerful CPUs and RAM — apply directly to training throughput too. You get all of that included in the NVIDIA B300 server price, so the training performance increase you see is larger than the price jump from NVIDIA H100, which pushes your actual cost-effectiveness higher rather than lower.
Most cloud providers currently only offer NVIDIA Blackwell-generation GPUs through long-term reservations, which makes sense for hyperscalers managing supply but is a poor fit for teams that want to test a workload before committing to a 12-month contract.
Hyperstack offers NVIDIA B300 GPU on-demand pricing at $7.40 per GPU, per hour. The 2.3 TB, 64 TB/s, 144 PFLOPS and 14.4 TB/s NVLink figures above are for a full 8-GPU server, so size your test to match how you plan to deploy. That means you can rent an NVIDIA B300 GPU on-demand, benchmark your actual workload rather than a synthetic one and scale up only once you know the numbers work for your use case.
If you are deciding whether to rent an NVIDIA B300 GPU on-demand or commit to a reserved contract, the on-demand path is the lower-risk way to answer the question that actually matters: does the 3.6x memory increase and the doubled interconnect speed translate into real cost savings for your specific model and traffic pattern. You can run that test on Hyperstack before deciding how much capacity to commit to long term.
For teams evaluating NVIDIA B300 GPU specs against their current NVIDIA H100 footprint, on-demand access also removes the biggest blocker to testing early: procurement timelines. You do not need to sign a reservation to find out whether your inference stack benefits from up to 288 GB of memory per GPU.
Not every workload needs an NVIDIA B300. If you are running small models, batch jobs that do not care about latency, workloads that already fit comfortably on an NVIDIA H100 with room to spare, INT8-quantised inference or FP64 and HPC workloads, the NVIDIA H100 is still the more cost-effective choice today. The NVIDIA B300 earns its price when one of three things is true.
Your models no longer fit in 80 GB of GPU memory without splitting across multiple GPUs and taking a communication penalty. Your inference workload needs longer context windows or higher token throughput than your current fleet can deliver. Or you are training a model where memory and compute are the actual bottleneck, not just the price per hour.
Teams building generative AI products, reasoning models or multi-modal systems tend to hit all three of these limits at once, which is why the NVIDIA B300 GPU for generative AI use cases has moved from a nice-to-have to a near requirement for teams shipping frontier-scale models.
NVIDIA B300s land on-demand in August on Hyperstack. While most providers require reservations for NVIDIA Blackwell-generation GPUs, we offer on-demand access to NVIDIA B300 GPUs at $7.40 per GPU, per hour, so you can benchmark real workloads, deploy AI workloads on NVIDIA B300 without a long-term commitment and scale into reserved capacity once you have the numbers to justify it.
If your workloads outgrow on-demand capacity or you need guaranteed access, workload isolation or compliance controls that only dedicated hardware can provide, Hyperstack also offers NVIDIA B300 clusters through Secure Private Cloud. That gets you a private, single-tenant NVIDIA B300 cluster built around your specific training or inference needs with none of the noisy-neighbour risk that comes with shared capacity.
Yes. Hyperstack offers NVIDIA B300 GPUs on-demand at $7.40 per GPU, per hour, with no minimum contract term. This lets teams benchmark real training or inference workloads before committing to longer-term NVIDIA B300 capacity.
NVIDIA B300 on-demand pricing on Hyperstack is $7.40 per GPU, per hour. You can start with the GPU capacity your workload requires, benchmark performance and scale once you have real cost and performance data.
Hyperstack provides on-demand access to NVIDIA B300 GPUs, allowing teams to test and deploy generative AI and reasoning workloads without first signing a long-term reservation. This is ideal when you need to validate whether the NVIDIA B300's 288 GB of HBM3e memory improves model fit, context length or inference throughput.
The NVIDIA B300 comes with a configuration of 8 GPUs as a single cluster and provides up to 288 GB of HBM3e memory per GPU, giving larger models more room to run without being split across multiple GPUs. On Hyperstack, you can benchmark your actual model on an NVIDIA B300 before deciding whether additional GPU capacity is required.
If your workload is constrained by the 80 GB memory of an NVIDIA H100, the NVIDIA B300's up to 288 GB of HBM3e memory can significantly change what fits on a single GPU. Hyperstack's on-demand access lets you test the same workload on an NVIDIA B300 and measure the impact before changing your infrastructure.
Yes. Hyperstack's on-demand NVIDIA B300 access lets you benchmark real workloads without a long-term commitment. You can test model fit, inference throughput and the impact of the NVIDIA B300's higher memory and interconnect bandwidth before deciding whether to move to longer-term or dedicated capacity.
Yes. If your workload outgrows shared on-demand capacity or requires guaranteed access and workload isolation, Hyperstack also offers NVIDIA B300 clusters through Secure Private Cloud. These provide dedicated, single-tenant NVIDIA B300 infrastructure for larger training and inference deployments.
A standard NVIDIA B300 server uses 8x NVIDIA B300 GPUs, providing up to 2.3 TB of aggregate GPU memory. The system also provides 14.4 TB/s of NVLink bandwidth, which is designed to support workloads that need high-speed GPU-to-GPU communication. Hyperstack can provide NVIDIA B300 capacity for teams evaluating these larger configurations.
No. Hyperstack does not charge ingress or egress fees, so network data transfer does not add separate ingress or egress costs to your NVIDIA B300 workload.