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NVIDIA B300 GPUs are a strong choice for large MoE models, image and video generation, and long-context workloads. NVIDIA GB300 NVL72 connects 72 GPUs through NVLink in a single rack, which makes it a better fit for very large frontier-scale models and reasoning models with long chains of thought. In this post, we’ll break down the differences between B300 and GB300, explain how memory and NVLink affect performance, and cover how replica sizing and parallelism help you increase throughput per GPU and reduce latency.
2.8 trillion parameters. That’s the size of the largest open-source large language model: Kimi K3. Today, larger GPU systems make deploying models at this scale possible. In this post, we break down the differences between B300 and GB300 to help you find which GPU is best for your use case. Here’s how they compare.
System configuration overview
*or across nodes over InfiniBand/Ethernet.
B300 vs. GB300 architecture differences
A GPU node is a physical server with CPUs, system RAM, local storage, networking, and one or more GPUs. B300 GPUs are deployed in nodes containing 8 GPUs each. A GB300 NVL72 has 72 B300 GPUs across 18 nodes in a single rack. A rack holds multiple nodes.
B300 nodes use x86 CPUs, while GB300 NVL72 systems use NVIDIA Grace CPUs. Grace CPUs have a very high-bandwidth interconnect between the CPU and the GPU, which makes it efficient to move KV-cache data into the larger CPU memory when GPU memory is limited.
The tradeoff is software compatibility: Grace CPUs use ARM64 rather than x86, so the software stack must support ARM64.
How does NVLink connect the GB300 rack?
NVLink is NVIDIA’s high-bandwidth GPU-to-GPU connection. In a GB300, NVLink connects 72 B300 GPUs. Each GPU still has its own local HBM, and NVLink allows the GPUs to exchange data directly between their local HBM instead of going through CPU memory. This speeds up distributed inference, where a model runs across multiple GPUs and nodes.
A GB300 NVL72 does not have to run one model across all 72 GPUs. The rack can be divided into multiple four-GPU, eight-GPU, or larger replicas. For most models, smaller replicas maximize throughput per GPU, and larger replicas can reduce latency for individual requests.
Once the replica is chosen, parallelism divides the model’s work across the GPUs:
Tensor parallelism: splits computations (large matrix multiplies) across GPUs.
Pipeline parallelism: When a model is too large to fit on one GPU, pipeline parallelism splits the model by layers across multiple GPUs.
Expert parallelism: distributes entire experts from MoE models across different GPUs. On a GB300 NVL72, wide EP spreads experts across dozens of GPUs over NVLink, which frees memory for larger batches and increases throughput.
Context/sequence parallelism: splits the input sequence and its KV-cache data across GPUs.
Note: B300 nodes also use NVLink, but only among the eight GPUs in a node. Traffic between nodes goes over slower InfiniBand or Ethernet. GB300 NVL72 uses NVLink across all 72 GPUs in the rack. Only traffic between racks goes over InfiniBand or Ethernet.
Which system should you choose?
You should pick the system that matches your model size, traffic volume, and budget.
B300 is great for:
Large MoE text models in FP4
Image generation
Video generation
Long-context workloads: long prompts, document processing, and codebase analysis.
GB300 is great for:
Very large frontier-scale models
Reasoning models with long chains of thought
FAQ
How do I know if my workload is prefill-heavy or decode-heavy?
Compare input length to output length. Long documents, codebases, RAG pipelines, and long chat histories with short responses are prefill-heavy and benefit from compute (B300's strength). Short prompts with long generations (e.g. creative writing, code generation, reasoning models) are decode-heavy and bound by memory bandwidth instead.
What is a MoE model and why does it need so much memory?
A mixture-of-experts (MoE) model splits its feed-forward layers into many specialized "experts" and only activates a few per token, so it gets the quality of a huge model at the compute cost of a small one. The catch: all experts must sit in GPU memory even though only a fraction are active at any moment. A model might use just 30B parameters per token but still require memory for 600B+ total, which is why frontier MoE models demand systems like the GB300 NVL72.
NVLink vs InfiniBand: what's the difference?
NVLink connects GPUs directly to each other's memory within a node or rack, delivering far higher bandwidth and lower latency than any network. InfiniBand is a network fabric that connects separate servers across a data center. Inside a GB300 NVL72, all 72 GPUs communicate over NVLink; InfiniBand only comes into play when scaling beyond a single rack. For inference, keeping a model inside the NVLink domain avoids the much slower hop over the network.
What should I measure when testing B300 or GB300 for my workload?
Test with your model, prompt lengths, output lengths, and expected traffic. Measure time to first token, time between output tokens, and throughput at your target concurrency. Don’t compare GPUs on hourly price alone. Compare how much it costs to process a million tokens while keeping response times within your requirements.
