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For a new AI platform centered on reasoning inference, long context, or high concurrency, B300 is the strongest fit when memory capacity and Blackwell Ultra attention performance justify its facility demands. B200 is the more balanced Blackwell option—especially for mixed AI and FP64 workloads—while H200 can make sense for teams prioritizing continuity with an existing Hopper environment. There is no universal winner: model size, precision, GPU-to-GPU communication, power and cooling, and the cost of changing infrastructure all affect the choice.
What separates B300, B200, and H200?
These are data-center GPU platforms, not ordinary add-in cards that can be selected on a single performance number. Memory determines whether model weights and working data fit; memory bandwidth affects how quickly data can be moved; and NVLink supports communication among GPUs in a scale-up system. Precision support and performance matter too, but a peak figure does not predict application throughput by itself.
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Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →The generations also imply different operational choices. B300 and B200 are Blackwell-family options with fifth-generation NVLink, while H200 is a Hopper-family option with fourth-generation NVLink. For an organization already running Hopper systems, the value of preserving its established software, drivers, tools, and cluster procedures may matter as much as a hardware specification. A greenfield system has fewer continuity constraints and can weigh the newer platforms more directly.
Specifications at a glance
The figures below come from NVIDIA’s HGX AI Factory component table, HGX Platform material, Blackwell Ultra datasheet, DGX B300 product page, and DGX B300 data-center best-practices document. Memory figures for B300 are deliberately shown with their source context: NVIDIA publishes different capacities for different configurations.
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| Specification | B300 | B200 | H200 |
|---|---|---|---|
| GPU family | Blackwell Ultra | Blackwell | Hopper |
| Per-GPU HBM in NVIDIA’s HGX component table | 288 GB HBM3e | 180 GB HBM3e | 141 GB HBM3e |
| Eight-GPU aggregate HBM in that table | 2.30 TB | 1.44 TB | 1.1 TB |
| Other published B300 memory specification | 270 GB HBM3e per GPU in NVIDIA’s Blackwell Ultra datasheet | Not stated in the cited datasheet | Not stated in the cited datasheet |
| Memory bandwidth per GPU | 7.7 TB/s in NVIDIA’s Blackwell Ultra datasheet | Up to 8 TB/s in NVIDIA’s HGX component table | 4.80 TB/s in NVIDIA’s HGX component table |
| NVLink generation | Fifth | Fifth | Fourth |
| GPU-to-GPU NVLink bandwidth | 1.8 TB/s | 1.8 TB/s | 900 GB/s |
| Aggregate NVLink bandwidth in an eight-GPU reference platform | 14.4 TB/s | 14.4 TB/s | 7.2 TB/s |
| Sparse FP4 peak in NVIDIA’s HGX Platform table | 144 PFLOPS | 144 PFLOPS | Not stated in the cited table |
| FP64 / FP64 Tensor Core figure in NVIDIA’s HGX Platform table | 10 TFLOPS | 296 TFLOPS | Not stated in the cited table |
| Attention performance comparison in NVIDIA’s HGX Platform table | 2× B200 | Reference for the comparison | Not stated in the cited table |
| Published GPU power limit | Configurable up to 1,100 W in the Blackwell Ultra datasheet | Not stated in the cited sources | Not stated in the cited sources |
Peak PFLOPS figures are vendor specifications, and the FP4 values are explicitly sparse. They should not be read as measured tokens per second, training time, or application throughput. Results depend on the model, precision, kernels, parallelism, system configuration, and software.
How to choose by workload
Choose B300 for memory-constrained reasoning and inference
B300 is the leading candidate when model weights, long context, or a large number of concurrent requests make GPU memory the constraint. More HBM can give a deployment room for larger model states or working sets, but it does not guarantee that any particular model will fit: actual memory use depends on model architecture, precision, batching, context length, and runtime overhead.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteNVIDIA positions DGX B300 for generative AI and reasoning workloads, and its HGX Platform table lists twice the attention performance of B200. Those claims strengthen the case for evaluating B300 in attention-heavy inference, but they do not replace a representative workload test. If memory is not the bottleneck, B300’s capacity advantage may not translate into enough benefit to offset the power, cooling, and system-level requirements.
Choose B200 for a balanced Blackwell system or mixed AI and HPC
B200 is a strong middle option when the objective is to deploy Blackwell with fifth-generation NVLink without selecting B300’s maximum-memory configuration. Its listed memory bandwidth is up to 8 TB/s per GPU, and it shares the HGX Platform table’s sparse FP4 peak figure with B300. That makes it relevant for both AI training and inference, subject to application-specific performance.
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The clearest specification-based reason to favor B200 is FP64: NVIDIA’s HGX Platform table lists 296 TFLOPS for B200 FP64/FP64 Tensor Core, compared with 10 TFLOPS for B300. For a mixed AI/HPC environment where FP64 work is material, that is a meaningful distinction. It is not proof that B200 wins every scientific or HPC application; kernels, precision requirements, parallelism, and the rest of the system still determine actual results.
Choose H200 when Hopper continuity is worth more than a generational change
H200 remains a reasonable option for teams extending a Hopper deployment or prioritizing continuity in software and operations. It provides 141 GB HBM3e per GPU in NVIDIA’s HGX component table, making it a large-memory Hopper path for workloads that fit the platform and benefit from that capacity.
Its fourth-generation NVLink figures are below the fifth-generation figures NVIDIA lists for B200 and B300. That makes H200 less compelling for a new scale-up system whose priorities are maximum inter-GPU bandwidth or Blackwell-era low-precision features. It may nevertheless be the sensible choice where the cost and risk of moving to a new platform outweigh the benefits of newer hardware. The available specifications do not establish a purchase-price advantage for H200.
What the memory numbers mean in practice
GPU memory is often discussed as though it were one simple capacity figure, but NVIDIA publishes B300 figures that differ by source and product context. Its HGX AI Factory component table lists 288 GB HBM3e per B300 GPU, while the Blackwell Ultra datasheet lists 270 GB HBM3e and 7.7 TB/s bandwidth per GPU. Do not combine those into a single universal B300 specification or assume they describe the same configuration. When sizing a purchase, confirm the exact system and GPU configuration in the vendor’s current bill of materials and technical documentation.
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The system-level numbers need similar care. The HGX component table’s eight-GPU aggregate is 2.30 TB for B300, whereas NVIDIA describes DGX B300 as an eight-GPU enterprise system with 2.1 TB of total GPU memory. Those are figures for separately named product contexts, not interchangeable descriptions of one universal eight-GPU build. The same table’s B200 and H200 aggregate figures are 1.44 TB and 1.1 TB respectively.
Aggregate HBM across GPUs is not automatically one pool that every application can use without coordination. Model-parallel and distributed workloads can divide weights or computations among GPUs, but the software has to manage that distribution and communicate as needed. Capacity planning should therefore use the usable memory and parallelism behavior of the intended system and runtime—not only multiply a per-GPU figure by eight.
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For multi-GPU jobs, the path between GPUs can matter as much as the performance of one GPU. NVIDIA lists fifth-generation NVLink on B300 and B200 at 1.8 TB/s GPU-to-GPU bandwidth, with 14.4 TB/s aggregate bandwidth in the eight-GPU reference platform. H200’s fourth-generation figures are 900 GB/s GPU-to-GPU and 7.2 TB/s aggregate in that reference context.
These values describe the interconnect specifications, not the speed of every distributed workload. A job that frequently exchanges activations, synchronizes, or shares data may be more sensitive to interconnect behavior than one that spends most of its time computing locally. The realized benefit also depends on how the server is configured and whether the software can keep the GPUs usefully occupied. Before choosing on NVLink alone, identify how often the target workload communicates across GPUs and benchmark it on a representative multi-GPU configuration.
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Precision: FP4, FP8, FP16/BF16, and FP64
Lower-precision arithmetic can improve throughput or reduce memory use for AI workloads when the model and software support it with acceptable quality. NVIDIA’s HGX Platform table reports 144 PFLOPS sparse FP4 for both B300 and B200. B300 is distinguished there by its reported attention advantage over B200, not by a higher listed sparse FP4 peak.
FP4 is not a universal setting to apply blindly. The model, inference or training framework, numerical behavior, and accuracy target determine whether a low-precision path is useful. Buyers should verify support in their intended software stack and check output quality as well as speed. The cited figures do not provide comparable FP8 or FP16/BF16 performance values across all three GPUs, so they cannot establish a full ranking at those precisions.
For FP64-intensive work, the published B200-to-B300 difference is substantial: the HGX Platform table lists 296 TFLOPS FP64/FP64 Tensor Core for B200 and 10 TFLOPS for B300. That makes B200 the more natural of these two Blackwell choices for workloads where that specific FP64 capability is important. The cited material does not provide an equivalent H200 FP64 figure, so it does not support a three-way FP64 ranking.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Power, cooling, and rack planning
Power and heat are purchase constraints, not details to resolve after selecting a GPU. NVIDIA’s DGX B300 best-practices document estimates typical system power at roughly 14.5–15 kW and peak power at approximately 19–19.7 kW, depending on configuration. It discusses air and rear-door heat-exchanger configurations. Those are system-level estimates, not the power draw of one GPU, and the peak is not the same as typical operation.
NVIDIA’s Blackwell Ultra datasheet says B300 can be configured up to 1,100 W TDP per GPU. That configurable GPU limit does not replace the system power estimates: a complete server has other components and the actual configuration matters. The cited material does not provide comparable system-power figures for B200 and H200, so it would be misleading to infer their facility requirements from B300’s DGX numbers.
Before committing to a B300 deployment, have facilities and infrastructure teams validate the exact system configuration, power delivery, cooling approach, rack integration, and operating envelope with the system supplier. A platform that cannot be supported at the intended site is not a practical performance upgrade. The same discipline applies to any large data-center GPU purchase; this evidence does not establish a detailed power or cooling comparison across all three models.
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NVIDIA’s DGX B300 product page says the eight-GPU system is available now. That statement concerns DGX B300; it does not establish availability, delivery timing, or allocation for every B300 configuration or for B200 and H200 systems in every region. Confirm current supply and delivery terms with the relevant system provider.
The cited official material does not establish purchase prices, lead times, or a comparable total cost of ownership for B300, B200, and H200. A GPU-only price comparison would also miss costs that can decide the project: the server configuration, facility upgrades, power consumption over the planned operating period, cooling, software work, utilization, and the operational cost of changing platforms. Get like-for-like system quotations and include the cost of making the site ready.
For an existing Hopper cluster, include migration work and operational risk in the H200-versus-Blackwell decision. For a new system, compare the cost of a configuration sized for the actual workload rather than assuming the largest memory option is automatically the best value. These are project-specific inputs; the published specifications alone cannot settle the economics.
A practical decision process
- Define the workload. Record whether the system is for training, inference, reasoning, mixed AI/HPC, or a combination; identify target model sizes, context lengths, concurrency, and precision requirements.
- Find the limiting resource. Determine whether the workload is constrained by HBM capacity, memory bandwidth, compute, GPU-to-GPU communication, or facility power and cooling. Do not use a peak specification as a substitute for measurement.
- Screen for precision and software fit. Confirm that the intended framework and kernels support the precision modes and GPU platform you plan to deploy, and validate model quality where lower precision is involved.
- Compare the systems you can actually buy. Ask suppliers to identify the exact GPU and server configuration, usable memory, interconnect, power envelope, cooling design, availability, and support terms. Keep HGX component figures separate from DGX system specifications.
- Test representative jobs. Measure the application’s throughput, latency, quality, and utilization on a representative configuration. Include multi-GPU behavior if the production job will use multiple GPUs.
- Model the full deployment cost. Include procurement, site preparation, power and cooling, software and migration work, and expected utilization over the project’s life. Select the configuration that meets the workload and facility requirements—not simply the one with the largest specification.
Verdict
B300 leads when the job benefits from maximum memory capacity, Blackwell Ultra’s attention performance, and low-precision reasoning or inference—and the organization can support the system’s power and cooling needs. B200 is the more balanced Blackwell choice and the strongest specification-based fit here for mixed AI and FP64 workloads. H200 remains relevant for Hopper continuity, but its lower listed NVLink bandwidth makes it a harder fit for a greenfield deployment whose priority is scale-up performance. Validate exact configurations and representative workloads before buying; the published peaks and memory totals are not a substitute for that decision.
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