NVIDIA B300 vs. NVIDIA B200: Blackwell Ultra vs. Blackwell

The Blackwell architecture arrived in 2024 as NVIDIA's answer to the next era of AI compute. The B200 set a new standard for inference performance, memory capacity, and training throughput, and many teams are still ramping up their use of it today.

Then came Blackwell Ultra.

The B300 is built on the same silicon foundation as the B200: same dual-reticle die design, same TSMC 4NP process node, same NVLink 5 interconnect. But NVIDIA didn't start from scratch; they pushed the existing architecture to its ceiling, targeting the one thing that changed fastest since Blackwell launched: the memory and compute demands of AI reasoning at scale.

This blog sets the two chips side by side, breaks down what's actually different (and what isn't), and helps you work out which generation makes sense for your workloads.

What is the NVIDIA B200?

The B200 is NVIDIA's flagship Blackwell GPU, the first generation of the Blackwell architecture, and the chip that defined a new baseline for AI infrastructure in 2024 and 2025.

The B200 introduced a dual-reticle die design that houses 208 billion transistors across two dies, connected via NVIDIA's High-Bandwidth Interface (NV-HBI) at 10 TB/s. Together, those dies operate as a single CUDA-programmed accelerator: no software changes, no new frameworks, full backward compatibility.

At its launch, the B200 delivered a step change over the Hopper generation:

• 192GB HBM3e per GPU: 2.4x more memory than the H100
• 8 TB/s of memory bandwidth: 2.4x the H100's 3.35 TB/s
• 10 petaFLOPS of dense NVFP4 compute: 5x more than Hopper's FP8 throughput
• NVLink 5 at 1.8 TB/s per GPU: Double the Hopper NVLink bandwidth

For the workloads that defined 2024 and early 2025, large language model inference, generative AI, and training runs at scale, the B200 hit hard.

What is the NVIDIA B300 (Blackwell Ultra)?

The B300, also known as Blackwell Ultra, is the highest-performance GPU in the Blackwell lineup. Where the B200 was a clean generational leap from Hopper, the B300 is a targeted push on the dimensions that started to constrain the B200 as AI reasoning workloads took hold.

The headline changes are three:

The B300 is built for the workload landscape that emerged after Blackwell launched: reasoning models, agentic AI pipelines, and frontier LLMs that stress memory and attention far more than raw FP8 throughput.

Key characteristics:

• 288GB of HBM3e arranged in eight 12-Hi stacks, across an 8,192-bit total width, 3.6x more memory than the H100, and 50% more than the B200
• 15 petaFLOPS of dense NVFP4 compute, using the same fifth-generation Tensor Cores as the B200 but driven harder at the chip level
• Doubled SFU throughput for exponential operations, enabling 2x faster softmax and attention-layer computation, the specific operations that become the bottleneck in long-context reasoning
• 8 TB/s memory bandwidth, unchanged from the B200, meaning the B300's additional memory is accessed at the same speed, the gain is headroom and capacity, not raw bandwidth
• NVLink 5 at 1.8 TB/s per GPU, supporting configurations of up to 576 GPUs in a non-blocking compute fabric, the same scale-out architecture as the B200
• Up to 1,400W TGP, a modest step up from the B200's 1,200W, reflecting the higher sustained compute utilization the B300 is designed to maintain

NVIDIA B300 vs. NVIDIA B200: Spec comparison

The numbers tell a clear story. The B300 and B200 share a significant amount of silicon: same architecture, same process, same interconnect, same memory bandwidth. The B300's gains are concentrated in the three dimensions that reasoning-era AI workloads actually stress: how much model you can hold in memory, how fast you can run inference on it, and how fast attention computes across long sequences.

Which one should you choose?

The answer depends on what your workloads actually look like.

The B200 is the right call if:

• Your models fit comfortably in 192GB per GPU, with headroom to spare
• Your inference workloads are standard request-response, without long chain-of-thought sequences
• You want access to the Blackwell generation now, at a lower entry price
• Your bottleneck is FP8 training throughput, where B300 and B200 are equivalent

The B300 is worth the upgrade if:

• You're running large reasoning models (DeepSeek-R1, large MoE architectures) where 192GB becomes the constraint
• Your context windows are long enough that softmax and attention computation is a measurable part of wall-clock inference time
• You need to serve more concurrent inference sessions without adding GPUs
• You're running 300B+ parameter models and want them fully resident on the GPU without offloading

In practice: if your workload already presses the B200 on memory, or if you're building for reasoning-model inference rather than standard generation, the B300 is where you should be. If you're still scaling into Blackwell and your models don't need 288GB, the B200 is still an extremely capable GPU and available today.

Summary

The B300 isn't a replacement for the B200 so much as a response to what the B200 revealed: that as reasoning models arrived and context windows grew, memory capacity and attention throughput became the binding constraints faster than anyone expected.

NVIDIA's answer was to keep everything that was working (the dual-die design, the TSMC 4NP process, NVLink 5, the CUDA ecosystem) and push hard on the three numbers that were limiting the most demanding workloads: memory, NVFP4 compute, and attention speed.

Both GPUs are available on Civo today with transparent pricing, no egress surprises, and pre-installed NVIDIA drivers so you go from provisioned to running in minutes.

→ Get a Blackwell Ultra B300 GPU today with Civo

→ Get a Blackwell B200 GPU today with Civo

FAQs