NVIDIA B200 vs. B300: Does Your AI Workload Need Blackwell Ultra?

NVIDIA's Blackwell-generation accelerators are built for the largest-scale AI workloads in today's inference era.

The NVIDIA B200 brought the Blackwell architecture to data center GPUs, delivering major gains in AI compute and memory bandwidth over its predecessors. The B300 takes that foundation further with Blackwell Ultra, adding more GPU memory and substantially more compute.

Both GPUs are a strong fit for exascale-style deployments, but the differences between them become crucial when you're pushing the limits of model size, context length, and inference throughput. Here's how the B200 and B300 stack up, and which workloads each one is best suited for.

NVIDIA B200: The Blackwell Foundation

The flagship GPU of the original Blackwell generation, the NVIDIA B200 was designed to handle the growing compute and memory demands of ultra-high-performance computing and agentic AI systems.

NVIDIA's Blackwell architecture features a dual-die design rather than a single die as in previous generations. Two reticle-sized dies are linked with a 10 TB/s chip-to-chip interconnect, creating a unified GPU with 208 billion transistors.

The B200 also provides up to 192 GB of HBM3e memory with 8 TB/s of memory bandwidth, compared to the H200's 141 GB at 4.8 TB/s bandwidth. This substantial increase gives large models more room to run on a single GPU.

The performance gains over NVIDIA's Hopper generation are significant. On InferenceMAX v1 benchmarks, the B200 delivered more than four times the per-GPU throughput of the H200 on a Llama 3.3 70B workload, reaching 10,000 tokens per second at 50 tokens per second per user.

Image source: NVIDIA

Because Llama 3.3 70B is a dense model with a large parameter count and uses all of its parameters simultaneously during inference, this benchmark highlights Blackwell's ability to accelerate demanding, compute-intensive workloads with innovations like native NVFP4 support.

To sum up, some key B200 specifications include:

• 192 GB of HBM3e memory at 8 TB/s bandwidth.
• Native NVFP4 support for high-throughput AI inference.
• 208 billion transistors across two reticle-sized dies connected by a 10 TB/s chip-to-chip interconnect.
• 1.8 TB/s of fifth-generation NVLink bandwidth per GPU for intra-node communication.
• 3x the training performance and 15x the inference performance of a DGX H100 system when deployed in a DGX B200 multi-GPU node architecture.

The Blackwell B200 is a generational leap beyond Hopper. But as models grow larger and inference workloads become more demanding, even 192 GB of GPU memory can become a limiting factor.

That's where Blackwell Ultra comes in.

NVIDIA B300: Next-Level Performance with Blackwell Ultra

Building on the Blackwell foundation, the B300 introduces the Blackwell Ultra architecture. It takes the same dual-die design of its predecessor and significantly increases the resources available to power next-generation AI workloads.

The biggest difference is memory capacity. The B300 packs a massive 288 GB of HBM3e memory, 96 GB more than the B200, while maintaining the same 8 TB/s memory bandwidth.

Image source: NVIDIA

With 50% more memory capacity than the B200 and twice that of the H200, the Blackwell Ultra B300 has a lot more room for larger model weights, higher batch sizes, and extending context length without KV-cache offloading. This additional space means it can support more advanced reasoning models and serve more requests without having to distribute workloads across additional GPUs.

For instance, with Mixture-of-Experts models like Llama 4 Maverick, where all parameters must be loaded into memory even though only a fraction of the model is active during any given calculation, the B300's extra VRAM can make single-GPU serving possible at INT4 precision in cases where even the B200's memory is insufficient.

Blackwell Ultra also delivers a meaningful compute jump over the original Blackwell generation. NVIDIA specifies 15 PFLOPS of dense NVFP4 compute for Blackwell Ultra, compared with 10 PFLOPS for Blackwell, a 50% increase. It also delivers up to 2x faster attention-layer compute. These gains give the B300 an advantage for high-throughput inference and other workloads that become compute-bound at larger scales.

Image source: NVIDIA

A few notable B300 features include:

• 288 GB of HBM3e memory at 8 TB/s bandwidth, providing 50% more capacity than the B200.
• Up to 15 PFLOPS of dense NVFP4 compute, a 50% increase over Blackwell for high-throughput inference.
• 640 fifth-generation Ultra Tensor Cores and up to 160 streaming multiprocessors.
• ConnectX-8 networking with up to 1.6 Tb/s bandwidth in DGX B300 systems.
• 2x the attention-layer acceleration and 1.5x the AI compute FLOPS of the original Blackwell architecture.

However, there are trade-offs. Blackwell Ultra can reach up to 1,400 W TGP, and DGX B300 systems come in at around 14 kW system power. That can make deployment significantly more complex for teams running their own infrastructure. Power delivery, cooling, rack design, and facility planning must be considered alongside the B300's performance gains.

B200 vs. B300: Full Spec Comparison

The B300 comes out ahead on multiple metrics, but not every workload needs the additional capabilities of Blackwell Ultra.

B200 vs. B300 Use Cases: Which GPU Is Right for Your Workload?

The right choice depends on where your workload is hitting its limits and how close you are to running out of headroom.

The B200 may be the right fit if you:

• Need Blackwell's FP4 capabilities and high memory bandwidth for enterprise-scale AI workloads without the additional capacity of the B300.
• Have models that fit comfortably within 192 GB of GPU memory and don't anticipate memory requirements to increase significantly.
• Are scaling across multiple GPUs and prefer a more manageable power and cooling profile per machine.
• Want strong production AI performance without the higher costs or lead times that come with Blackwell Ultra.

The B300 is likely the right choice if you:

• Need to fit larger models within a single GPU, particularly those that approach or exceed the B200's 192 GB memory limit.
• Are running long-context or high-concurrency inference workloads where additional VRAM for KV cache and batching can prevent memory from becoming the bottleneck.
• Require maximum inference throughput for high-volume workloads where the B300's additional compute can serve more requests per GPU.
• Want to future-proof AI infrastructure for increasingly complex agentic and reasoning workloads.

The Bottom Line

The NVIDIA B200 and B300 share a similar Blackwell foundation, but they're designed for different workload requirements.

The B200 gives you FP4 performance and 192 GB of headroom for workloads that don't push past that limit. With air or liquid cooling options depending on configuration, it offers a more manageable infrastructure profile while delivering the substantial performance gains that made Blackwell a major step forward from Hopper.

The B300, on the other hand, is built for when memory becomes the bottleneck. Its massive 288 GB of memory and higher compute throughput make it better suited for larger models, longer contexts, higher concurrency, and frontier inference workloads where the B200's memory or compute ceiling falls short. But it comes at the cost of substantial power and cooling demands.

Buying either GPU outright means committing to that infrastructure decision for years and also requires a significant upfront investment. For many AI teams, the cost is prohibitive. Vast.ai solves that problem.

Renting GPU compute on Vast.ai means you can run B200s for everyday enterprise-scale training and inference, move to B300s when you need the extra memory and throughput, and switch back anytime while paying only for the compute you use.

Compare current B200 and B300 pricing on Vast.ai, or any other NVIDIA GPU in our lineup. From H200s to A100s, RTX 5090s, and more, explore available instances to find the right fit for your workload today.