NVIDIA B300 Explained: Specs, Use Cases, and Why It Exists

GPU infrastructure used to have a clear job:
serve models fast and handle as many requests as you could throw at it.

The H100 generation was built for that, and it did it well.
The AI workloads worth talking about now need something more.

Reasoning models don’t just generate text – they plan, verify, loop back, and work through multi-step chains of logic before producing a response.

Agentic pipelines run multiple models in sequence across contexts that can stretch to hundreds of thousands of tokens. These workloads eat memory, demand sustained throughput over longer inference windows, and make
the economics of the previous GPU generation look worse with every passing quarter.

The NVIDIA B300, built on the Blackwell Ultra architecture, is designed around this new reality.

288GB HBM3e: When The Model Finally Fits

The memory constraint has been the quiet tax on serious AI inference for a long time. Running a 70B parameter model on H100s meant planning for multi-GPU setups, accounting for the overhead that comes with sharding, and accepting that some of your memory bandwidth is going to inter-GPU communication rather than actual computation. At 100B+ parameters, the math gets harder.

At 200B+, you’re essentially building infrastructure around the constraint
rather than around the workload itself.

The B300 ships with 288GB of HBM3e memory – more than three times the 80GB on a standard H100 SXM. That changes the size of problem that fits cleanly on a single GPU, and what “single GPU inference” even means in practice:

• Models up to roughly 150B parameters can run fully in memory without quantization or multi-GPU sharding.
• Long-context workloads – 500K+ token legal documents, full codebases, extended
  conversation histories – they stop being a memory management problem and start being a throughput problem.
• Multi-modal pipelines that process high-resolution video or image data alongside text gain headroom that changes what’s feasible in a single inference pass.
• Teams that were quantizing aggressively just to fit a model can now run at higher
  precision without giving up on single-GPU economics.

The Economics of Running Reasoning AI at Scale

Speed is the headline in most GPU announcements. But the more useful number for anyone making an infrastructure decision is cost per token – what it actually costs to serve a million requests, once you factor in power, hardware, and utilization. The B300’s numbers here are significant (compared to the Hopper generation):

• 50x higher throughput per megawatt – which means dramatically more work with the
  same power draw, and that shows up directly in operating costs for any team running at meaningful scale.
• 35x lower cost per token for agentic workloads specifically – the workloads that matter most right now, and the ones that were making H100 infrastructure economics look difficult.

For teams running a few hundred requests per day, this doesn’t change the conversation much. For teams running millions of inference calls monthly, or planning infrastructure that needs to handle that kind of scale – it’s the kind of number that restructures what’s worth building on.

What the B300 is Actually Built For

The B300 is a data center GPU – not a workstation card, and not aimed at development or fine-tuning workflows on a single machine. The workloads it’s designed for are the ones where memory, sustained throughput, and cost-per-token economics are all important at the same time:

• Frontier model inference – running 70B to 200B+ parameter models in production, where single-GPU memory makes the difference between a clean deployment and a multi-GPU sharding exercise.
• Reasoning and agentic AI pipelines – multi-step workloads where the model loops, verifies, and calls external tools, and where inference windows are long enough that sustained throughput matters as much as peak speed.
• Long-context inference – legal document review, financial analysis, extended code generation, and other tasks where context windows stretch to hundreds of thousands of tokens and need to sit cleanly in memory.
• Multi-modal at scale – vision-language models and video understanding pipelines where the combined memory footprint of image data and language context has historically been the limiting factor.
• Large-scale fine-tuning – teams that need to adapt frontier models to their domain and want the memory headroom.

Coming to Neysa Velocis

The B300 is coming to Neysa Velocis. If you want to be among the first to access B300 capacity on Velocis when it goes live, get in touch with the team.