Nvidia H200 vs B200: The Practical 2025 Guide To Specs, Price, And What Changes

Introduction

GPU shopping used to be simple. You picked the fastest card you could afford, shoved it into a server, and tried not to think about power bills. Now it’s weirder. Peak FLOPS are still up and to the right, but your day-to-day pain is down in the plumbing: memory capacity, bandwidth, interconnect, first-token latency, and the awkward reality that “deployment” is a facilities project.

That’s why the Nvidia H200 vs B200 decision isn’t a nerdy benchmark argument. It’s a choice between two styles of waiting. H200 is the “ship it in normal racks” option. B200 is the “buy the platform, not the card” option. And in the background, Blackwell Ultra (think B300-class systems) is Nvidia’s bet that “reasoning” workloads will happily burn 10x to 100x more compute at inference time if it improves answers.

I’ll keep this grounded. We’ll walk from concepts to hard specs, then to practical tradeoffs, then to a verdict you can act on.

1. From The Memory Wall To The Reasoning Wall

For big model inference, the honest bottleneck has been memory. Not because compute is cheap, but because models keep getting wider and contexts keep getting longer. If weights and KV cache don’t fit, you end up paying a tax in offload latency, smaller batch sizes, and all the fun ways throughput collapses under real traffic.

Hopper’s late-cycle answer is H200. It’s basically the mature “stop starving the GPU” move: more HBM, more bandwidth, and a deployment story that still resembles the servers you already own.

Blackwell’s answer is more existential. Nvidia’s Blackwell Ultra datasheet frames inference-time scaling (long thinking, reasoning) as a third scaling axis, and says it can demand up to 100x more compute than one-shot inference. The implication is blunt: the future bottleneck is not just memory, it’s the cost of thinking.

That’s the context you need for Nvidia H200 vs B200. One is tuned for today’s memory wall, the other is tuned for tomorrow’s reasoning wall.

2. The H200 NVL In Plain English

When most people say “H200” in procurement meetings, they mean the H200 NVL: a dual-slot PCIe Gen5 card, passive air-cooled, designed to drop into enterprise servers that can push enough chassis airflow. In other words, the NVIDIA h200 tensor core gpu is a very modern part packaged in a very traditional way. Nvidia also calls it a “Tensor Core GPU” and positions it as optimized for LLM inference workloads.

The practical appeal is boring in the best way: it’s powerful, it’s familiar, and it doesn’t force you to rebuild your data center.

2.1. Nvidia H200 Specs That Actually Move Workloads

If you only remember two NVIDIA h200 specs, make them these:

• HBM3e capacity: 141 GB
• Peak HBM bandwidth: 4,813 GB/s (about 4.8 TB/s)

Those numbers matter because they let you keep more of the working set on device. Inference quality and latency are increasingly tied to long contexts, and long contexts are basically “KV cache inflation.” H200 gives you room to breathe.

2.2. The NVLink Detail People Skip

PCIe Gen5 is fine, but multi-GPU inference becomes interesting when the GPUs can actually talk. The H200 NVL product brief specifies a maximum NVLink bandwidth of 900 GB/s.

That’s not rack-scale magic, but it’s enough to make 2-way and 4-way bridged configurations feel meaningfully different from “eight GPUs sharing a PCIe switch and vibes.” If your team wants to scale inside normal servers, this is a key part of the Nvidia H200 vs B200 story.

3. What You’re Really Buying With B200

With B200, Nvidia’s unit of value shifts toward systems. The NVIDIA b200 gpu is less a single card story and more a node story. The flagship pitch is DGX B200: eight Blackwell GPUs, NVSwitch, and a lot of HBM bandwidth packaged as a deployable node.

On Nvidia’s DGX B200 page, the spec snapshot includes 1,440 GB total GPU memory, 64 TB/s HBM3e bandwidth, and 14.4 TB/s aggregate NVLink bandwidth. That’s a clue to what B200 is optimizing for: not “one GPU is fast,” but “a whole node behaves like a coherent GPU island.”

This is where people go hunting for NVIDIA b200 specs, because a generational jump only matters if it lands in your topology.

3.1. Nvidia B200 GPU: Per-GPU Anchors

For a per-module reference, Lenovo’s HGX B200 listing shows 180 GB HBM3e, 8 TB/s memory bandwidth, and 1000 W power. Those numbers explain the vibe shift. B200 class hardware is not quietly slipping into legacy racks, it’s asking you to take power and cooling seriously.

3.2. Nvidia B200 Release Date, And Why “Available” Is A Loaded Word

Nvidia announced Blackwell in March 2024 and described HGX B200 as a building block for new systems. That’s the marketing timeline.

The operational timeline is messier. Availability depends on OEM allocations, who gets early systems, and whether you’re buying “some GPUs” or “a fleet.” So yes, you can quote the NVIDIA b200 release date from announcements, but the date that matters is the day your vendor can ship racks at volume.

4. Architectural Showdown: Hopper Vs Blackwell, Minus The Fog

Here’s the clean mental model. Hopper, especially H200, is a bandwidth and capacity refinement. It attacks the memory wall so inference stops tripping over itself.

Blackwell shifts attention to inference-time scaling and aggressive low-precision math, because reasoning workloads make every inefficiency expensive.

4.1. Hopper’s Bet: Make Memory Less Of A Tax

H200 NVL delivers 141 GB HBM3e and 4,813 GB/s bandwidth. In a world where KV cache dominates memory footprints, this is not a detail, it’s the product.

4.2. Blackwell Ultra’s Bet: Assume The Model Will Think Longer

Blackwell Ultra is explicit about “long thinking,” and it pairs that with a very direct spec: up to 20 petaFLOPS of FP4 sparse inference performance and 279 GB of HBM3E memory on a single GPU.

Even if you’re focused on Nvidia H200 vs B200, Blackwell Ultra matters because it shows where the roadmap is pointing: bigger memory per GPU and lower precision designed for inference efficiency.

4.3. The Interconnect Escalation

H200 NVL’s bridging tops out at 900 GB/s. Blackwell Ultra lists fifth-generation NVLink at 1.8 TB/s on the GPU interconnect line. That’s the architectural posture shift: treat GPU-to-GPU communication as a first-class resource, not an afterthought.

5. Performance Claims You Can Map To Work

Benchmarks are useful when they match your workload shape. Otherwise they’re just vibes in bar-chart form.

5.1. H200’s Real Win: Throughput Under Memory Pressure

Nvidia positions H200 NVL as optimized for LLM inference, emphasizing compute density and high memory bandwidth. Translated: if you’re bandwidth-bound serving large models, H200 is designed to raise the ceiling without forcing you into exotic system designs.

5.2. Blackwell Ultra’s Real Win: AI Factory Output

Blackwell Ultra’s GB300 NVL72 description cites rack-scale numbers: up to 37 TB of high-speed memory per rack, 1.44 exaFLOPS of compute, and a unified 72-GPU NVLink domain. It also claims up to a 50x overall increase in AI factory output performance versus Hopper-based platforms.

Is that universally true? No. It’s scenario-specific and projected. But it tells you what Nvidia wants you to optimize: throughput plus responsiveness at scale.

5.3. Where B200 Lands

DGX B200 is pitched as 3x training performance and 15x inference performance over DGX H100. Treat that as a direction, not a law of physics, and then ask a better question: does your workload benefit more from bigger coherent nodes or from easy incremental upgrades?

That’s the core Nvidia H200 vs B200 decision.

6. Power And Cooling: The Truth You Learn After The PO Is Signed

If your job includes “owning the outage,” you already know where this is going.

6.1. H200: Fits Normal Enterprise Constraints

The H200 NVL is dual-slot, PCIe Gen5, passive air-cooled, and designed around system airflow. It’s still a lot of watts, but it’s a kind of watts your current rack strategy might survive.

6.2. Blackwell Ultra: Assumes A Different Facility

GB300 NVL72 is described as fully liquid-cooled and rack-scale, integrating 72 Blackwell Ultra GPUs with 36 Grace CPUs.

This is where Nvidia H200 vs B200 can become a non-technical decision. If you cannot support higher density or liquid cooling, the most brilliant FP4 numbers in the world won’t help.

6.3. NVIDIA b200 vs h100 Upgrade Reality

Plenty of teams are really asking about NVIDIA b200 vs h100, because nobody wants to buy “the last good Hopper” if the fleet is about to flip to Blackwell.

Here’s the honest rule of thumb:

• If your facility and procurement pipeline are optimized for H100-era systems, H200 is usually the lowest-risk step.
• If you’re already rebuilding infrastructure and buying integrated nodes, B200 makes more sense.

It’s not romance. It’s engineering and logistics.

7. Nvidia H200 vs B200: Price, Without Pretending There’s A Single Sticker

Let’s talk about NVIDIA b200 price and cost expectations without hallucinating a retail tag. Nvidia does not publish a neat, universal MSRP list for these accelerators. What you get is quotes, bundles, and the occasional “allocation fee” dressed up as market dynamics.

Still, we can triangulate.

• One set of industry estimates places H200 purchase costs roughly in the $30k to $40k range per GPU. Treat this as directional.
• Another estimate suggests B200 modules can land around $45k to $50k for certain OEM quotes, while noting Nvidia has not officially published standalone pricing.
• A separate analysis estimates $30k to $40k per B200 and roughly $515k for a DGX B200 system, again as an estimate rather than a quote sheet.

The right way to think about Nvidia H200 vs B200 pricing is cost per useful token at your target latency. If B200 class systems raise throughput per rack, the economics can win even when the invoice looks outrageous.

8. Availability: When Geopolitics Touches Your Lead Time

Specs don’t matter if you can’t get the hardware. Reuters reported in December 2025 that Nvidia was evaluating adding production capacity for H200 after demand from Chinese customers exceeded current output levels, following U.S. approval to export H200 to China with a 25% fee. Reuters also notes limited H200 quantities in production as Nvidia prioritizes newer product lines, plus uncertainty around Chinese government approval.

In plain terms: the Nvidia H200 vs B200 decision can get forced by supply. Sometimes the “best GPU” is the one you can actually buy at scale this quarter.

9. Feature Spotlight: Video Generation And The 4-Million-Token Surprise

LLMs are not the only token hog in town anymore. Video diffusion and world-model generation can be brutally expensive, and they change what “real time” means.

Blackwell Ultra’s datasheet claims a five-second video generation sequence can process about 4 million tokens, taking nearly 90 seconds on Hopper, while Blackwell Ultra enables real-time video generation with a 30x performance improvement versus Hopper.

If your roadmap includes media generation, this is where Nvidia H200 vs B200 stops being an LLM-only argument. You’re choosing how quickly you want to step into the next workload class.

10. Table 1: Spec Snapshot You’ll Actually Reference

This table mixes card-level and platform-level numbers on purpose. That’s how these products are evaluated in the real world.

11. Table 2: Decision Matrix For Humans With Deadlines

Use this as a sanity check when the Nvidia H200 vs B200 thread starts spiraling in Slack.

12. Conclusion: Pick The GPU That Buys Back Your Time

Here’s the clean takeaway for Nvidia H200 vs B200. H200 is the peak of the “make deployment sane” era. You get 141 GB of HBM3e at 4.8 TB/s, plus a PCIe, air-cooled form factor that fits into enterprise reality.

B200 is the start of the “buy the factory” era. You’re paying for coherent multi-GPU islands, massive aggregate bandwidth, and a roadmap built around systems, not single cards.

So don’t overthink it. Write down four numbers: your model size, context length, target latency, and what your racks can physically handle. That’s enough to decide Nvidia H200 vs B200 without a single benchmark argument.

If you want a concrete recommendation, drop your workload shape (model, context, concurrency, tokens per second targets) plus your power and cooling constraints. I’ll translate that into a realistic Nvidia H200 vs B200 deployment plan, with a topology suggestion and the few sharp edges you’ll want to avoid.