B200 vs B300: Blackwell vs Blackwell Ultra

NVIDIA B200 and B300 sit one generation step apart inside the Blackwell family. B200 introduced the Blackwell architecture to large-scale AI infrastructure; B300, branded Blackwell Ultra, is aimed more heavily at reasoning, long-context inference and workloads that benefit from substantially more accelerator memory.

The most important practical difference is not the product name. It is memory capacity and the type of workload you are trying to run.

Memory: B300 has much more headroom

NVIDIA's current HGX reference architecture lists B200 SXM with 180 GB of HBM3e per GPU and B300 SXM with 288 GB of HBM3e.

That extra memory matters for large models, longer context windows, larger KV caches and workloads where keeping more of the model or intermediate state on each accelerator reduces communication overhead.

See the individual NVIDIA B200 and NVIDIA B300 profiles for the current specifications stored in GPU Data Hub.

Memory bandwidth

Both HGX B200 and HGX B300 are listed by NVIDIA at up to 8 TB/s of GPU memory bandwidth.

That means the upgrade is not simply "faster memory." B300's advantage comes from the larger memory footprint plus architectural improvements targeted at newer AI workloads.

Why B300 targets reasoning workloads

Reasoning models can spend much more compute during inference than conventional one-pass generation. Long prompts, extended chains of thought, tool use and test-time scaling can increase both memory and compute requirements.

NVIDIA describes Blackwell Ultra as a platform for AI reasoning and test-time scaling inference. Its current performance material says B300 offers higher dense FP4 performance and stronger attention performance than B200.

For buyers, this means B300 is most interesting when inference itself has become a major compute workload rather than merely a lightweight deployment step after training.

B200 still matters

B300 does not make B200 obsolete.

B200 remains a very high-end accelerator with 180 GB of HBM3e and broad suitability for training and inference. Because it arrived earlier, B200 capacity can also be easier to find or priced differently depending on the cloud provider and contract structure.

If a workload fits comfortably in B200 memory and does not gain much from Blackwell Ultra's reasoning-focused improvements, the cheaper available option may deliver better economics.

Cloud pricing

GPU cloud pricing changes quickly, especially for recent accelerators.

At the time of each update, GPU Data Hub records provider-published prices separately rather than assuming that one provider's rate represents the market. Use the GPU Price Tracker to compare current B200 and B300 listings and check each row's last-updated timestamp.

Do not compare only the headline hourly rate. Check:

• whether the rate is per GPU or per multi-GPU instance
• whether it is on-demand, spot or reserved
• CPU and system memory included
• NVLink and node topology
• networking between nodes
• storage and egress costs
• region
• minimum cluster size
• actual capacity availability

Training

For large-scale training, both B200 and B300 can be used in dense multi-GPU systems.

B300's larger memory can let teams increase batch sizes, reduce some forms of model partitioning, or fit workloads that would otherwise require more accelerators. The actual benefit depends heavily on model architecture and parallelism strategy.

The interconnect and complete server design matter as much as the individual accelerator. Always compare the full HGX, DGX or cloud instance configuration.

Inference

B300's strongest case is high-end inference, especially reasoning and long-context workloads.

More HBM per accelerator can support larger models, bigger KV caches and more concurrent inference state. NVIDIA also positions Blackwell Ultra specifically around improved inference and attention performance.

For smaller models or straightforward batch inference, B200 may already provide more than enough performance.

Which should you choose?

Choose based on the bottleneck.

If you need maximum memory per GPU, long-context serving or high-end reasoning inference, investigate B300 first.

If your workload fits within 180 GB per GPU and B200 capacity is materially cheaper or easier to reserve, B200 can still be the more economical choice.

Before committing, compare the live B200 and B300 cloud prices, inspect the GPU cloud provider directory, and calculate the full project cost with the GPU cloud cost calculator.

For a previous-generation comparison, see B200 vs H200.

Primary technical references

NVIDIA publishes the underlying hardware specifications in its HGX AI Factory reference architecture and Blackwell Ultra documentation. GPU Data Hub uses those current NVIDIA specifications for the hardware figures above rather than relying on third-party spec aggregators.