| Feature | NVIDIA H200 | NVIDIA DGX B200 |
|---|---|---|
|
GPU Architecture |
Hopper |
Blackwell |
|
Memory Capacity |
141GB HBM3e per GPU |
1,440GB total across 8 GPUs |
|
Inference Performance |
2X vs. H100 in LLM throughput |
15X vs. H100 for inference |
|
Training Performance |
Accelerates LLMs like Llama2 70B |
3X training speed over previous-gen DGX |
|
Form Factor & Deployment |
SXM/PCIe flexibility, air-cooled options |
10RU unified platform with integrated CPUs, storage, and networking |
|
Power Consumption |
600–700W per GPU |
14.3kW max system load |
|
Use Case Fit |
High-throughput inference, scalable HPC |
End-to-end enterprise AI pipelines |
|
Software Ecosystem |
NVIDIA AI Enterprise, NIM microservices |
Full-stack suite with Mission Control and DGX OS |
|
Scalability Options |
NVIDIA MGX & HGX certified systems |
DGX BasePOD & SuperPOD hyperscale setups |
|
Best For |
Modular upgrades, inference-first workloads |
AI factories, enterprise-scale AI operations |
1. AI Performance: LLM Inference, Training, and Versatility
If you’re deploying or scaling enterprise generative AI, both the H200 and DGX B200 offer next-gen performance – though in very different packages.
- DGX B200 (Blackwell Architecture): Up to 3x training speed and 15x inference performance compared to previous-gen systems. Designed as an all-in-one AI factory, the B200 excels in full-pipeline workloads—LLM training, recommender systems, chatbots, and more.
- H200 (Hopper Architecture): Features 141GB of HBM3e memory at 4.8TB/s bandwidth, delivering 2X inference throughput for models like Llama2 70B compared to the H100. Built for large-scale inference and HPC acceleration.
Best Choice?
- For training + deployment on one platform, DGX B200 is ideal.
- For high-volume, low-latency inference or HPC tasks, the H200 leads in memory bandwidth and throughput.
2. Architecture & Compute Power: Blackwell vs. Hopper
These GPUs represent NVIDIA’s top-tier innovation in AI Hardware.
- DGX B200: Packs 8x Blackwell GPUs, 72 PFLOPS training, 144 PFLOPS inference, and up to 1,440GB total GPU memory—a staggering footprint for enterprise-grade AI.
- H200: Offers 4 PFLOPS FP8, 3,958 TFLOPS Tensor performance, and multi-instance support with up to 7 MIGs per GPU. Perfect for flexible deployments across LLMs, RAG, and computer vision.
Best Choice?
- Choose DGX B200 for GPU density and scale, especially in mission-critical AI factories.
- Choose H200 for cutting-edge memory and compute flexibility, especially where energy efficiency and form factor matter.
3. Scalability & Integration: NVLink, PCIe, and Multi-GPU Setup
How do these systems expand across your data center?
- DGX B200: Uses 5th-gen NVLink and fits within DGX SuperPOD configurations for hyperscale environments. Includes 2x Intel Xeon CPUs and up to 4TB system memory—built to integrate seamlessly.
- H200: Comes in SXM and NVL form factors, supporting PCIe Gen5, NVLink bridge up to 900GB/s, and works with NVIDIA MGX and HGX platforms.
Best Choice?
- DGX B200 is turnkey for full-stack deployments
- H200 provides modularity and flexibility for targeted upgrades or edge integration.
4. Energy Efficiency & Cost Optimization
AI acceleration without ballooning your power budget
- DGX B200: Draws 14.3kW max power – high performance, enterprise-class load.
- H200: Operates at 600-700W per GPU with better TCO and energy efficiency than the H100.
Best Choice?
- DGX B200 is purpose-built for high-density AI pipelines where energy scaling is already factored in.
- H200 is ideal for lower-power deployments or fitting existing infrastructure.
5. Software Ecosystem & Enterprise Support
Performance is nothing without operational excellence.
- DGX B200: Includes NVIDIA Mission Control, AI Enterprise stack, and lifecycle services—perfect for CIOs and federal IT leads managing compliance and uptime.
- H200: Bundled with a five-year AI Enterprise subscription, plus support for NIM microservices—great for fast-tracking AI development and integrating secure, production-ready workloads.
Best Choice?
- DGX B200 suits organizations investing in full-stack AI infrastructure.
- H200 works for those seeking targeted acceleration and robust software support in smaller rack footprints.
Final Verdict: DGX B200 or H200?
Choose DGX B200 if:
- You want a unified AI platform for training, deployment, and scaling.
- You’re building out a mission-critical AI factory with consistent workload needs.
- Your organization requires dense compute, maximum memory, and end-to-end support.
Choose H200 If:
- You need high-throughput inference and HPC acceleration in a scalable form factor.
- You prioritize energy efficiency and modular integration into existing infrastructure.
- Your use case focuses on LLM deployment, scientific computing, or edge workloads.
Final Thoughts – Tailor the Power to the Mission
The DGX B200 is like owning a full-stack AI assembly line, while the H200 acts as a performance tuned tool ideal for slicing through inference workloads and HPC bottle necks. Whether you’re a federal integrator, data scientist, or enterprise strategists, the right choice depends on your pipeline stage, workload diversity, and deployment goals. If you have any questions, contact us today!
Frequently Asked Questions
Read what common questions our customers have about the NVIDIA H200 vs DGX B200.
What’s biggest the difference between NVIDIA H200 and DGX B200?
The H200 is a powerful, memory-rich accelerator based on the Hopper architecture, ideal for high-throughput inference and memory-intensive HPC tasks. DGX B200 is a turnkey platform built around eight Blackwell GPUs with 3X training and 15X inference performance over previous-gen systems.
Is H200 better for inference?
Yes, especially for large language models like Llama2 and GPT-3. The H200 delivers 2X inference performance over the H100, with optimized throughput and low latency for real-time results.
Can DGX B200 be used for inference too?
With 144 petaFLOPS of inference power and unified memory across eight Blackwell GPUs, the DGX B200 is capable of handling massive inference workloads.
Which system is better for training large AI models?
Ace supports NIST SP 800-171 and SP 800-53 controls, enabling secure deployment across federal networks.
Which system has better memory bandwidth?
The H200 features 141GB of HBM3e and 4.8TB/s bandwidth—nearly double the H100 and essential for accelerating HPC and AI inference. DGX B200 offers more total GPU memory (1,440GB), but bandwidth per GPU is lower compared to the H200’s newer memory spec.
Is the H200 more energy efficient?
Yes. Despite its power, the H200 maintains the same thermal profile as the H100, with configurable TDP and higher energy efficiency—especially important for enterprise deployments prioritizing reduced TCO and sustainability.
Is DGX B200 scalable?
Extremely. It’s designed for integration into NVIDIA BasePOD and SuperPOD clusters, offering hyperscale deployment for enterprise AI factories. If you’re building end-to-end solutions, from training to inference, the DGX platform is unmatched.
Which system should I choose for AI development and deployment?
Choose the H200 if you need targeted, scalable inference, enhanced memory bandwidth, and flexible form factors. Choose DGX B200 if you’re deploying a full-stack AI workflow, require the highest performance for training, and need integrated software and support for enterprise-scale operations.
