The pace of innovation in AI is accelerating—and so are the demands on your infrastructure. As models grow in size and complexity, the need for faster training, higher memory bandwidth, and scalable performance has become mission-critical. For teams currently running on H100 or H200 GPUs, the newly released NVIDIA B200 isn’t just an upgrade—it’s a necessity.

At ionstream.ai, we’re working with AI developers and infrastructure teams who are pushing the limits across LLMs, generative models, and multimodal AI. The common thread? H100s are starting to fall short—whether it’s due to slow training times, compute bottlenecks, or the high operational cost of scaling workloads. That’s where the B200 comes in.

Built on NVIDIA’s Blackwell architecture, the B200 delivers up to 20 petaFLOPS of FP8 compute, a 5x increase over the H100. That’s not just a spec sheet boost—it translates into up to 3x faster training performance in real-world workloads, giving teams a measurable edge when training foundation models or scaling large-scale LLMs.

Inference performance is even more impressive. Early testing across production pipelines shows up to 15x acceleration vs. H100, helping AI teams deploy models with greater efficiency, less tuning, and faster time to results.

As models grow, memory limitations become the real bottleneck. The B200 solves for that with 192GB of HBM3e memory and 5.3 TB/s bandwidth—a 67% improvement over the H100. This means developers can run longer context windows, increase token throughput, and reduce I/O delays that commonly slow down training.

With an upgraded Transformer Engine and a dedicated decompression pipeline, the B200 also accelerates attention computation by up to 4x—ideal for demanding generative workloads.

Each B200 delivers enough power to replace multiple H100s, allowing teams to simplify their GPU infrastructure while increasing performance. The result? Lower energy consumption, reduced hardware overhead, and cost-effective scaling for compute-heavy pipelines.

Even more important: the B200 is significantly more eco-friendly than its predecessor. For AI teams looking to reduce their environmental impact without compromising performance, the B200 stands out as the truly green choice. It’s a win for your workloads—and for the planet.

Teams we work with are already seeing the impact: fewer machines to manage, less power draw, and faster turnaround across both training and inference workflows.

Unlike typical cloud-based solutions, Ionstream.ai gives you direct, on-demand access to bare metal B200 GPUs. No shared resources. No virtualization layers. Just raw performance when you need it most.

Because we manage the infrastructure ourselves, we’re able to bypass long allocation queues, reduce lead times, and give you more control over your environment. If you’re already working with H100 or H200 GPUs, switching to B200 through Ionstream means faster time to training and fewer compute headaches.

B200 demand is rising fast. Teams moving early are already gaining a competitive advantage in both performance and operational efficiency. With limited availability and growing interest from AI dev teams, LLM startups, and infrastructure leads, now is the time to make the switch.

For those prioritizing performance and sustainability, the B200 offers the rare combination of speed, scale, and eco-conscious design.

H100 vs. B200 at a Glance:

• 3x faster training performance 
• 15x faster inference throughput 
• 67% more memory bandwidth 
• 192GB HBM3e for larger models and context windows 
• Lower TCO with better power efficiency 
• Significantly greener than the H100—truly the eco-friendly choice

Sign up now to get direct access to NVIDIA B200 GPUs with IonStream.ai — no queues, no delays, just pure compute power when you need it most. Start now at ionstream.ai