NVIDIA Blackwell GPU Comparison: B200, B300, GB200, GB300

The AI infrastructure market has never moved faster, and the GPU choices available to enterprise teams have never been more consequential. NVIDIA’s Blackwell architecture introduced four distinct products (the B200, GB200 NVL72, B300, and GB300 NVL72), each optimised for a different point on the performance and deployment spectrum.

For teams building at the scale of an AI factory, the wrong choice affects more than benchmark numbers. It affects model training timelines, inference economics, data centre power requirements, and the geographic reach of your AI products. This guide explains what each GPU is designed for, how they compare on the metrics that matter, and why enterprises are increasingly deploying through Axe Compute’s Build to Order program.

What Is an AI Factory?

NVIDIA defines an AI factory as a data centre infrastructure system designed to produce AI outputs at scale: training foundation models, running inference workloads, and enabling enterprise AI applications across an organisation. According to NVIDIA, an AI factory “provides the integrated compute, networking, storage, and software to operate like a hyperscaler.”¹

In practice, an AI factory is what separates a team running isolated GPU workloads from an organisation that has made AI compute a core operational capability. It requires dedicated hardware, high-bandwidth interconnects, and infrastructure provisioned specifically for the volume and continuity of AI workloads that production-scale organisations run.

The GPU family at the centre of most AI factory deployments in 2025 and 2026 is NVIDIA’s Blackwell architecture. Within that family, four products serve different deployment profiles: the B200, the GB200 NVL72, the B300, and the GB300 NVL72.

The Four NVIDIA Blackwell Products Explained

1. B200: The Production Workhorse

The B200 is the standard Blackwell GPU for data centre deployments. With 192 GB of HBM3e memory and approximately 9 petaFLOPS of FP4 dense compute, it is the most widely available Blackwell product and the baseline against which the rest of the family is compared.

The B200 suits teams that need strong Blackwell-generation performance across a range of workloads (inference serving, fine-tuning, and moderate training runs) without the scale-up architecture of the NVL72 rack systems. It operates within a 1,000 W TDP, making it compatible with a broader range of data centre power configurations.

• Best for: Large-scale inference, fine-tuning, multi-tenant deployments
• Memory: 192 GB HBM3e
• Power: 1,000 W TDP
• Interconnect: NVLink within multi-GPU systems

2. GB200 NVL72: Rack-Scale for Large Model Inference

The GB200 is a superchip that combines one NVIDIA Grace CPU with one Blackwell B200 GPU on a single module, connected by NVLink-C2C at 900 GB/s. The GB200 NVL72 assembles 72 of these superchips into a single rack system, delivering 1.4 exaFLOPS of FP4 performance across the full rack.²

The tight CPU-GPU integration is designed for workloads where data movement between processor and accelerator is a bottleneck, primarily very large language model inference and training at frontier scale. The GB200 NVL72 is a rack-scale commitment: it requires purpose-built infrastructure, higher power density, and specialist cooling. Teams deploying it are typically running models too large to fit efficiently on standalone multi-GPU nodes.

• Best for: Frontier model inference, very large model training, reasoning AI
• Memory: 13.4 TB HBM3e across the NVL72 rack
• Performance: 1.4 exaFLOPS FP4 (NVL72)
• Architecture: 72 Grace CPU + Blackwell GPU superchips per rack

3. B300 (Blackwell Ultra): Next-Generation Standalone

The B300, marketed as Blackwell Ultra, is NVIDIA’s next-generation standalone GPU, announced at GTC 2025 and shipping now. It increases HBM3e memory to 288 GB per GPU and delivers 14 to 15 petaFLOPS of FP4 dense compute, approximately 55 percent more than the B200. The DGX B300 system packs eight B300 GPUs for a total of 2.1 TB of GPU memory and 144 petaFLOPS of FP4 inference performance.³

The B300 is designed for the current era of AI reasoning: larger models, longer context windows, and more computationally intensive inference chains. One important trade-off is FP64 performance: the B300 drops from 37 TFLOPS on the B200 to approximately 1.25 TFLOPS to achieve its FP4 headroom. Teams with HPC or scientific workloads requiring FP64 precision should note this limitation.

The B300 also draws 1,400 W per GPU, compared to 1,000 W for the B200. Data centres deploying B300 at scale require higher power density and more advanced cooling infrastructure than B200 deployments.

• Best for: Large model inference, reasoning AI, training runs requiring larger memory per GPU
• Memory: 288 GB HBM3e per GPU
• Performance: 144 PFLOPS FP4 (8-GPU DGX B300 system)
• Power: 1,400 W TDP per GPU (~14 kW per 8-GPU system)
• Note: FP64 performance significantly reduced vs B200

4. GB300 NVL72: Exascale Reasoning

The GB300 NVL72 is the rack-scale successor to the GB200 NVL72, combining the Grace CPU with the Blackwell Ultra B300 GPU in a 72-superchip rack system. NVIDIA positions it as the foundation for the age of AI reasoning, delivering 1.44 exaFLOPS of FP4 sparse — or 1.08 exaFLOPS dense, a 50 percent improvement over the GB200 NVL72 — with 20 TB of HBM3e GPU memory and 1.8 TB/s of NVLink bandwidth per GPU.⁴

The GB300 NVL72 is the top of NVIDIA’s current product stack: a frontier system designed for organisations training or running inference on trillion-parameter models, multi-modal systems, and long-context reasoning engines. Like the GB200 NVL72, it is a rack-scale commitment with corresponding infrastructure requirements.

• Best for: Frontier model training, exascale inference, next-generation AI factories
• GPU Memory: 20 TB HBM3e (rack total)
• Performance: 1.44 exaFLOPS FP4 sparse | 1.08 exaFLOPS FP4 dense (NVL72 rack)
• NVLink bandwidth: 1.8 TB/s per GPU
• Architecture: 72 Grace CPU + Blackwell Ultra GPU superchips per rack

Side-by-Side Comparison

Table 1 — NVIDIA Blackwell GPU comparison: specifications by product

Sources: NVIDIA product pages for DGX B300, GB200 NVL72, GB300 NVL72. Specifications subject to change.

Which GPU Is Right for Your Workload?

The choice between these four products comes down to four variables: model size, inference latency requirements, data centre infrastructure constraints, and deployment timeline.

The B200 and B300 are standalone GPU deployments. The GB200 and GB300 NVL72 are rack-scale systems requiring purpose-built infrastructure. Choosing between the standalone and rack-scale families is a data centre architecture decision as much as a GPU selection decision.

Why AI Factories Choose Axe Compute’s Build to Order Program

Selecting the right GPU is only the first decision. For enterprises operating at AI factory scale, the harder problem is deploying that hardware exactly where it is needed, at high density, with compute that is always available, cannot be interrupted by other tenants, cannot be taken away when a contract cycles, and can grow and evolve as the business does. The value Axe brings is stability, reliability, and continuity. The Build to Order program is designed to deliver exactly that.

Deployed in Your Geography, on Your Terms

Most GPU cloud providers offer infrastructure in the regions where they have built data centres. For a global enterprise, this means fitting your AI factory requirements around someone else’s network map.

Axe Compute operates across 200+ locations in 93 countries. Under the Build to Order program, enterprise clients specify the GPU type and quantity they require, and Axe deploys that capacity in the specific data centres that serve their geographic needs, whether that is a single US location or a distributed deployment across the United States, Europe, and Asia for compliance, latency, or sovereign AI requirements.

Modern AI factory deployments are not single-region problems. A pharmaceutical company training models in the US and running inference in Europe and Asia needs GPU infrastructure in all three regions. Build to Order clients need all of this to work together — and it does.

Dedicated Capacity, Predictable Economics

Build to Order clients receive dedicated GPU capacity that is exclusively theirs. This means our clients do not share infrastructure with others, so there are no noisy neighbours and there is no risk of capacity being reallocated when demand spikes elsewhere on the network. The compute is there when they need it, and it stays there.

For organisations doing board-level budget planning, Series B due diligence, or enterprise procurement cycles, that predictability is a material operational advantage. GPU cloud spot prices for B200 and B300 have moved significantly in 2025 and 2026. A Build to Order deployment removes that exposure with flat-rate pricing that reflects actual cost structure — not the refinancing requirements of a leveraged balance sheet.

Axe Compute’s $260M enterprise contract demonstrates what this looks like at scale: a vetted enterprise client, a defined GPU configuration, and a dedicated deployment in the United States — delivering the stability, reliability, and predictability that production AI infrastructure demands.⁵

Hardware Upgrade Flexibility

GPU generations move fast. When Vera Rubin arrives and a client’s workload calls for it, Axe upgrades the infrastructure. The client does not need to renegotiate or restart. It just happens.

This is unusual in the market, and it is the right way to serve enterprise teams whose AI programs will span multiple GPU generations.

Vetted Access to Capacity That Is Not Publicly Advertised

Build to Order is not a self-serve product. Clients are vetted for deployment readiness, infrastructure maturity, and scale of commitment. This ensures that Axe deploys at the quality level the client requires, and gives enterprise clients access to GPU capacity(particularly B300 and GB300, which remain supply-constrained) that is not available through standard on-demand channels.

For AI factory teams that need a specific GPU configuration in a specific location with guaranteed availability, this is a material advantage over standard cloud procurement.

Sources

1. NVIDIA, “AI Factory.” nvidia.com/en-us/solutions/ai-factories/ ↩
2. NVIDIA, “GB200 NVL72.” nvidia.com/en-us/data-center/gb200-nvl72/ ↩
3. NVIDIA, “DGX B300.” nvidia.com/en-us/data-center/dgx-b300/ ↩
4. NVIDIA, “GB300 NVL72.” nvidia.com/en-us/data-center/gb300-nvl72/ ↩
5. Axe Compute, “$260M Enterprise Contract Announcement.” axecompute.com ↩