When it comes to scaling AI infrastructure, deciding whether to wait for the NVIDIA B300 or move ahead with the H200 or B200 isn’t just a purchase decision, it’s a strategic investment in your AI roadmap. If you’re running high-performance workloads or dealing with enormous models, understanding how each GPU affects your performance, scalability, and long-term viability is key.

Let’s cut through the hype and help you decide: should you deploy now, or hold out for the next leap in AI compute?

‍

H200 vs B200 vs B300: At-a-Glance Comparison

We’ve distilled the essential specs so you can compare your options:

‍

Availability & Pricing: What to Expect

Understanding lead times and price points is crucial for planning your infrastructure investments:

NVIDIA HGX H200:

• Lead time is 4-6 weeks. OEMs are gradually transitioning focus to B200, but stock is still flowing. Price point is in the mid $200K range.

NVIDIA HGX B200:

• Lead time is typically 3-4 weeks, depending on the OEM. We’ve had excellent results working with Aivres, which has delivered units quickly and reliably. Pricing usually falls in the mid $300K range.

NVIDIA HGX B300:

• Not expected until late 2025. Pricing will likely be $400K+ given its advanced scale and premium capabilities.

‍

‍

Why Choose H200?

The H200 is an ideal short-term upgrade path for teams currently running Hopper-based workloads via H100 GPUs. It delivers:

• More memory (141 GB HBM3e) for training larger models
• Faster bandwidth (4.8 TB/s) over H100
• ~1.4x performance gain without needing major infrastructure overhauls

‍

‍Perfect for:‍

• Teams that need a low-friction performance boost now without switching to a new architecture.

‍

‍

Why Choose B200?

The B200 is a performance leap. With a dual-die design, FP8/FP4 support, and ultra-high bandwidth, it provides:

• 2.5–3× speedup over H200
• 180 GB of HBM3e memory for heavy workloads
• Future-proofing for years of demanding AI projects

If you’re training LLMs, building AI inference platforms, or need next-gen throughput today, the B200 is ready.

‍

Ideal for:‍

• Forward-looking teams investing in scalable AI infrastructure in the immediate future.

‍

‍

Why Wait for B300?

The B300 is designed for AI at a planetary scale. With 288 GB of HBM3e and unprecedented AI compute, it is:

• ~1.5× faster than B200, ideal for trillion-parameter models
• Engineered for massive inference clusters and high-density training farms
• Projected to ship in late 2025 with pricing likely exceeding $400K

‍

Hold out only if:‍

• You’re running massive multi-node AI clusters and can delay deployment for another 6-12 months.

‍

‍

Choosing the Right GPU for Your AI Roadmap

Here’s a quick decision matrix:

Choose H200 if:

• You need more memory immediately.
• You want a safe upgrade from H100 without changing architecture.
• You’re under tight deployment timelines.

Choose B200 if:

• You want the best available AI performance today.
• Your models are already straining infrastructure limits.
• You’re investing in long-term scale with flexibility.

Wait for B300 if:

• You’re building for the extreme scale of next-gen AI.
• Your deployment can wait until late 2025.
• You need peak memory and compute density for future LLMs.

‍

‍

Wild Card Option: NVIDIA RTX PRO 6000 (Blackwell)

For teams focused on AI inference and looking for an alternative to SXM-based GPUs, the NVIDIA RTX PRO 6000 (Blackwell architecture) is a compelling option. These PCIe-based GPUs are significantly more budget-friendly than H200 or B200 servers, with 8-GPU systems starting at $115,000 USD, making them ideal for organizations scaling inference workloads without the capital demands of high-end server deployments.

Ideal for:‍

• Teams focused on inference-heavy tasks or edge deployments, and startups looking for performance without breaking the bank.

Key benefits include:

• Built on the Blackwell architecture for modern AI workloads
• High throughput with FP8/INT8 compute
• Plug-and-play PCIe form factor that integrates easily into existing infrastructure
• Excellent performance-per-dollar for inference at scale

Use case fit:‍

• Ideal for commercial applications where cost-efficiency, fast deployment, and power-optimized inference matter more than peak training throughput.

‍

‍

Arc Compute Can Help

At Arc Compute, we help teams navigate complex GPU decisions. Whether you’re upgrading, scaling, or preparing for next-gen AI, we can help you:

• Compare OEM lead times and pricing
• Optimize your deployment window
• Build a roadmap for hybrid GPU adoption

‍