B200 vs B300: Which Blackwell GPU Should You Choose?

B200 vs B300 is no longer just a spec comparison. For most teams, the real question is which Blackwell GPU better matches workload size, performance targets, and infrastructure readiness. In this article, FPT AI Factory breaks down the differences between B200 and B300 across compute, memory, power, and deployment fit.

1. What are the B200 vs B300 GPUs?

B200 vs B300 are NVIDIA Blackwell GPUs built for AI training, AI inference, and high-performance computing (HPC). While both belong to the Blackwell generation, they are designed for different workload profiles. 

• B200 is a Blackwell GPU built for enterprise AI workloads that need strong performance for training and inference, while keeping deployment more practical for a wider range of data center environments.
• B300 is a more advanced Blackwell GPU designed for larger and more demanding workloads, with stronger positioning for memory-intensive AI, large-model inference, and advanced HPC use cases.

Overall, B200 is often the more practical starting point for many AI teams, while B300 is better suited to organizations that need higher performance for more demanding workloads.

B200 and B300 are NVIDIA Blackwell GPUs for AI and HPC workloads (Source: FPT AI Factory)

2. B200 vs B300: Key Differences at a Glance

When comparing B200 vs B300, the main differences come down to memory, performance headroom, and infrastructure demand. B200 is the more balanced option for many enterprise AI workloads, while B300 is better suited to larger and more demanding deployments. 

3. B200 vs B300: Specs and Performance

3.1. Compute Performance

The B300 delivers roughly 30–35% higher peak throughput than the B200 across FP4, FP8, and INT8 precision formats. For FP64 — the precision standard for scientific and engineering workloads — the gap is even wider, with the B300 pulling ahead in double-precision math that HPC pipelines depend on.

That said, the B200 is no slouch. At FP8, it handles trillion-parameter model training effectively, and its FP4 support allows dense quantized inference that rivals previous-generation hardware at a fraction of the cost per token. For most production AI deployments today, the B200 already exceeds what teams can fully utilize.

The B300’s compute advantage becomes meaningful when running multiple large models simultaneously, training models above the 400–500 billion parameter range, or running tight-latency inference under heavy concurrency.

Compute performance between B200 and B300 (Source: Freepik)

3.2. Memory and Bandwidth

This is where the B300 most clearly separates itself. With 288 GB of HBM3e memory versus the B200’s 192 GB, the B300 can load significantly larger model weights without offloading to system memory – a bottleneck that silently degrades performance in large-scale inference.

Memory bandwidth follows the same pattern: 10 TB/s on the B300 versus 8 TB/s on the B200. In attention-heavy transformer workloads, bandwidth often constrains throughput more than raw FLOPS. A wider memory bus means faster data movement between GPU cores and memory, directly improving tokens-per-second on long-context models.

For teams running models that fit comfortably within 192 GB – including most 70B and many 180B parameter models — the B200’s memory is sufficient, and the cost delta from upgrading to a B300 may not justify the performance gain.

3.3. Power and Cooling Requirements

The B200’s ~1,000W TDP makes it compatible with many existing data center designs, particularly those already running A100 or H100 clusters. Standard air cooling can handle it in well-configured racks.

The B300, at ~1,400W, requires liquid cooling in most configurations. Retrofitting a facility for liquid cooling is a non-trivial investment – it affects rack design, power distribution units, facility cooling capacity, and maintenance protocols. Organizations without existing liquid cooling infrastructure should factor this into total cost of ownership before committing to a B300 deployment.

Power and Cooling Requirements (Source: Freepik)

4. B200 vs B300 for Different Workloads

4.1. AI Training and Inference

For training large language models (LLMs) and diffusion models, both GPUs are competitive. The B200 handles most current training jobs – including GPT-class models up to several hundred billion parameters — with strong throughput and reasonable power draw.

The B300’s larger memory and higher bandwidth pay off when training runs require holding bigger activation tensors or when gradient accumulation strategies push memory limits. Multi-node training that uses NVLink and NVSwitch also benefits from the B300’s higher inter-GPU bandwidth.

For inference, the choice depends on batch size and model size. Serving a 70B model at moderate concurrency? The B200 handles it well. Serving a mixture-of-experts model with a trillion-plus total parameters at high concurrency? The B300’s memory capacity prevents the performance degradation that comes from model sharding across too many GPUs.

Businesses that need to deploy AI training and inference quickly – without building or managing on-premise GPU infrastructure – can use GPU Virtual Machine from FPT AI Factory. This cloud deployment option gives teams immediate access to high-performance GPU compute, making it practical to start workloads within hours rather than months.

NVIDIA HGX B300 (Source: NVIDIA)

4.2. HPC and Scientific Computing

HPC workloads such as molecular dynamics, finite element analysis, climate modeling, and computational fluid dynamics rely heavily on FP64 precision. Here, the B300 has a clear advantage. Its higher double-precision throughput means shorter time-to-result for simulation runs that may take days or weeks at scale.

The B200 is still a capable HPC GPU – significantly better than the H100 at FP64 – but research teams running computationally intensive simulations will find the B300’s performance gap worth the infrastructure investment, particularly in academic and national lab environments where job queue times are a real operational constraint.

4.3. Quantized Inference and INT8 Workloads

Quantized inference – deploying models at INT8 or FP4 precision to reduce latency and cost – is one of the most common production AI use cases. Both the B200 and B300 support these formats natively, and the performance difference between them is proportional to the broader compute gap.

For inference-at-scale deployments where the goal is minimizing cost per query, the B200 may actually offer better economics: lower hardware cost, lower power draw, and sufficient throughput for most production traffic levels. The B300 is the right call when inference workloads are extreme – serving very large models, handling very high request volumes, or maintaining strict latency SLAs under load.

Quantized Inference and INT8 Workloads (Source: NVIDIA)

5. How to choose between B200 and B300

B200 and B300 are both built for large-scale AI, but they fit different deployment priorities. In practice, B200 is a strong choice for teams that need high-performance training and inference on a more established Blackwell platform, while B300 is better suited to reasoning-heavy and memory-intensive workloads that need higher throughput and larger memory headroom.

FPT AI Factory is expanding its GPU portfolio and will soon offer B300-based services for teams ready to take on the most demanding frontier AI and HPC workloads. If you want to get started now or be among the first to access B300 compute, reach out to our team.

The B200 vs B300 decision is ultimately about matching GPU capability to real workload requirements – and balancing that against infrastructure readiness. The B200 is the more accessible, cost-efficient choice for a wide range of AI training and inference tasks. The B300 is the right tool when memory capacity, bandwidth, or FP64 throughput becomes the limiting factor.

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• Email: support@fptcloud.com

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