NVIDIA B200 vs NVIDIA B300: Which Blackwell GPU Is Better for AI Inference?

Every inference team eventually faces the same question: do you need more compute or more memory?

NVIDIA's Blackwell generation gives you two answers within the same family. The NVIDIA B200 and NVIDIA B300 share the same architecture lineage and similar FP8 specifications, yet they are designed for different workloads and requirements. NVIDIA B300 adds more memory and higher dense FP4 throughput. One is built for the production inference workloads most companies are already running. The other is built for teams whose models have outgrown what a single GPU used to hold.

Our latest guide breaks down the real differences between the two and helps you determine which one fits your workload.

NVIDIA B200 Specs and Architecture 

NVIDIA B200 is built on the original NVIDIA Blackwell architecture and ships in servers of 8 GPUs. Each GPU carries 192 GB of HBM3e memory, and the 8-GPU server delivers 64 TB/s of aggregate memory bandwidth alongside 14.4 TB/s of NVLink bandwidth for GPU-to-GPU communication. NVIDIA reports 3x the training performance and 15x the inference performance of the previous NVIDIA H100 generation, driven largely by Blackwell's fifth-generation Tensor Cores and native FP4 support.

For most teams running production LLM inference, NVIDIA B200 is the GPU that replaced NVIDIA H100 as the default choice for new deployments. It handles the vast majority of generative AI, recommender system and chatbot workloads without the extra memory headroom that NVIDIA B300 provides and it does so at a lower hourly cost.

NVIDIA B300 Specs and Architecture 

NVIDIA B300 is built on Blackwell Ultra, a refinement of the same architecture tuned specifically for generative AI and reasoning workloads rather than a general refresh of NVIDIA B200. It ships in the same 8-GPU server configuration but each GPU carries 288 GB of HBM3e memory, which takes the full server to roughly 2.3 TB of aggregate GPU memory.

The extra memory does not come paired with a faster interconnect. NVLink bandwidth holds at 14.4 TB/s on both generations, and aggregate memory bandwidth holds at 64 TB/s. What changes is dense FP4 throughput, which rises from 72 PFLOPS on NVIDIA B200 to 108 PFLOPS on NVIDIA B300. That gain shows up directly in quantized inference workloads that lean on FP4 precision to serve models cost-effectively at scale.

NVIDIA B200 vs NVIDIA B300 Comparison

Here is how the two GPUs compare across the specifications that actually decide whether a workload benefits from each.

NVIDIA B200 vs NVIDIA B300 Memory

A 96 GB memory increase per GPU sounds incremental until you look at what it actually decides: whether a model has to be split across multiple GPUs at all. Splitting a model across GPUs adds a communication penalty every time activations cross the NVLink boundary, and that penalty compounds with every additional GPU a model needs. A model that just barely overflows 192 GB on NVIDIA B200 forces a two-GPU split. The same model on NVIDIA B300's 288 GB per GPU can often sit on a single device with room left over for KV cache and longer context windows.

That headroom matters most for two things: context length and batch size. Longer context windows and larger batches both consume memory before they consume compute, so a GPU with more memory per device can serve more concurrent requests or longer conversations without hitting an out-of-memory wall first. Teams running reasoning models or long-context retrieval-augmented generation pipelines tend to hit this wall earlier than teams running short-form chat or classification workloads.

NVIDIA B200 vs NVIDIA B300 Inference Performance and FP4 Throughput

For standard FP8 inference, the two GPUs perform almost identically. Both list 72 PFLOPS of sparse FP8 throughput and 36 PFLOPS dense, so a model already tuned for FP8 precision will not see a meaningful throughput gain from moving to NVIDIA B300. The gain appears specifically in FP4 dense throughput, where NVIDIA B300's 108 PFLOPS outpaces NVIDIA B200's 72 PFLOPS by 50%. If your inference stack already runs quantized to FP4 or you are planning to move it there to cut serving cost per token, that gap becomes the deciding factor.

Industry benchmarking backs up how much precision matters for inference economics broadly. According to SemiAnalysis InferenceX benchmarks from Q1 2026, NVIDIA Blackwell-generation systems serve gpt-oss-120B at roughly $0.02 per million tokens using NVIDIA TensorRT-LLM, compared to about $0.09 per million tokens on NVIDIA Hopper-generation systems running vLLM. That is close to a 4.5x improvement in cost per token, and it is the kind of gain that comes from precision and software optimization working together rather than from raw FLOPS alone. NVIDIA B300 pushes that same logic further for FP4-heavy workloads but only if your serving stack is actually built to use it.

NVIDIA B200 vs NVIDIA B300 Pricing 

On Hyperstack, NVIDIA B200 costs $6.00 per GPU per hour on demand, with reserved pricing starting from $5.10 per hour for longer-term commitments. NVIDIA B300 costs $7.40 per GPU per hour on demand, a difference of $1.40 per hour.

Across an 8-GPU server running continuously for a month, that gap adds up to a little over $8,000 in additional spend for the same runtime. Whether that premium is worth paying depends entirely on whether your workload needs the extra 96 GB of memory per GPU and the higher dense FP4 throughput. If it does not, NVIDIA B200 delivers the better cost per unit of performance today.

How to Benchmark NVIDIA B200 and NVIDIA B300 Before You Commit

Most cloud providers only offer NVIDIA Blackwell-generation GPUs through long-term reservations, which is a poor fit for teams that want to benchmark a workload before signing a 12-month contract. Hyperstack offers both NVIDIA B200 and NVIDIA B300 on demand, so you can run your actual model on each, measure tokens per second and memory headroom under your real traffic pattern and only scale into reserved or dedicated capacity once you know which GPU earns its price for your specific workload.

Conclusion

NVIDIA B200 and NVIDIA B300 are two configurations of the same NVIDIA Blackwell generation, differentiated mainly by memory capacity and dense FP4 throughput. If your models fit inside 192 GB and your inference runs on FP8, NVIDIA B200 remains the more cost-effective choice. If your models have outgrown that ceiling or your serving stack is built around FP4 precision, NVIDIA B300's extra memory and dense throughput justify the premium.

Hyperstack offers on-demand access to NVIDIA B300 GPUs at $7.40 per GPU, per hour, alongside NVIDIA B200 at $6.00 per GPU, per hour, so you can benchmark your own model on both before deciding which one to scale. If your workloads later need guaranteed capacity, workload isolation or compliance controls that only dedicated hardware provides, Hyperstack also offers both GPUs through Secure Private Cloud, built around your specific training or inference requirements.

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FAQs

What is the price difference between the NVIDIA B200 and B300 GPUs?

On Hyperstack, NVIDIA B200 costs $6.00 per GPU per hour on demand and NVIDIA B300 costs $7.40 per GPU per hour, a difference of $1.40 per hour. Reserved NVIDIA B200 pricing starts from $5.10 per hour for teams committing to longer terms. 

Is NVIDIA B300 faster than NVIDIA B200 for AI inference?

Not for standard FP8 inference. Both GPUs list identical FP8 throughput, so a model running at FP8 precision will see little to no speed difference between the two. NVIDIA B300 pulls ahead specifically on FP4-quantized inference, where its 108 PFLOPS of dense throughput beats NVIDIA B200's 72 PFLOPS by 50%, and on any workload that benefits from having more memory available per GPU.

How much memory does the NVIDIA B300 have compared to the NVIDIA B200?

Each NVIDIA B300 GPU carries 288 GB of HBM3e memory, compared to 192 GB on NVIDIA B200. That is 96 GB more per GPU, or roughly 768 GB more across a full 8-GPU server, which is the difference between a model needing to split across devices and fitting on one.

Which GPU should I choose for LLM inference at scale, NVIDIA B200 or NVIDIA B300?

It depends on your model's memory footprint and precision. If your models fit inside 192 GB and run at FP8, NVIDIA B200 gives you the better cost per token. If your models need more than 192 GB per GPU, or you are serving at FP4 precision at scale, NVIDIA B300's extra memory and dense FP4 throughput usually make it the more efficient choice despite the higher hourly rate.

Does NVIDIA B300 support FP4 precision better than NVIDIA B200?

Both GPUs support FP4 precision, but NVIDIA B300 provides higher dense FP4 throughput than NVIDIA B200. This matters most for production inference workloads that do not rely on sparsity to hit peak performance.

Can I rent NVIDIA B200 or NVIDIA B300 GPUs on demand without a long-term contract?

Yes. Hyperstack offers both NVIDIA B200 and NVIDIA B300 on demand at $6.00 and $7.40 per GPU per hour, with no minimum contract term. This lets you benchmark real training or inference workloads on either GPU before deciding whether to move to reserved pricing or a dedicated Secure Private Cloud deployment.