NVIDIA B200: The New Standard GPU for the Era of Large-Scale AI

1. The Era of Giant AI Models: A New Infrastructure Challenge

GPT-4, LLaMA 3, Claude 3 Opus—AI models with billions or even trillions of parameters are no longer exceptional.

But traditional GPU server infrastructure is hitting its limits:

Insufficient memory requires model sharding
GPU-to-GPU communication bottlenecks
Inadequate real-time inference speed
Rising power consumption and costs

Simply adding more GPUs no longer solves the problem.
What we need is a reimagining of AI infrastructure itself.
And NVIDIA’s answer is clear: the B200 GPU.

2. Introducing the NVIDIA B200: Powered by the New Blackwell Architecture

The NVIDIA B200 is a next-generation GPU based on the cutting-edge Blackwell architecture.
Compared to the previous-gen H100, the B200 offers:

More memory
Faster interconnects
Greater efficiency

Quick Specs: NVIDIA B200

Category	Spec
Architecture	Blackwell
GPU Memory	180GB HBM3E
FP8 Training	72 PFLOPS (across 8 GPUs)
FP4 Inference	144 PFLOPS (across 8 GPUs)
NVLink Bandwidth	1.8 TB/s
Form Factor	SXM6
Use Cases	Giant LLM training, ultra-low latency inference, AI simulation

3. H100 vs. B200: A Leap in Performance

Feature	H100	B200
GPU Memory	80GB	180GB
NVLink Bandwidth	900 GB/s	1.8 TB/s
FP8 Performance (1 GPU)	Up to 4 PFLOPS	Up to 18 PFLOPS
LLM Efficiency	Model sharding	End-to-end within a single instance

The B200 delivers up to 4.5× faster inference and 2.5× faster training compared to the H100.
This is a breakthrough, not just an upgrade.

4. Real-World Benchmark: Over 1,000 TPS with LLaMA 4 Maverick

According to NVIDIA’s official benchmark,
B200 GPUs achieved 1,038 tokens per second per user using Meta’s LLaMA 4 Maverick.

Why this matters:

No more chatbot delays
Real-time LLM for search and assistants
Scalable inference for thousands of concurrent users

5. Should You Switch to B200? Check These Boxes First:

The B200 isn’t just a high-performance chip—it’s the solution for infrastructure bottlenecks.
Here’s who should consider it:

Who Should Switch	Why
Giant LLM developers	Can train and infer GPT-4-scale models efficiently
Generative AI / SaaS	Lower latency and reduced operating costs
Research labs / Universities	Large-scale experimentation and fine-tuning
Biotech / Manufacturing	Real-time simulation + generative AI deployments
Cloud providers / AI farms	Robust backend for public LLM services

6. Release Timeline and Early Adoption in Korea

Announced: GTC March 2024
Global Availability: Early 2025
Korean Debut: February 2025 via AdTech firm PYLER
- Use case: Real-time video analytics and AI-powered ad targeting
- Result: Over 30× performance improvement over previous setup

The B200 is still in early adoption, but major Korean enterprises and public labs are moving quickly.

7. Two Flexible Ways to Use NVIDIA B200 with ELICE

ELICE helps you adopt B200 hardware without friction—whether you’re a startup or a data center operator.
Here are your options:

Option ①: Cloud-Based B200 GPU Instances

B200 GPUs available on-demand via ELICE Cloud:

No upfront hardware cost
Includes NVIDIA AI Enterprise suite
Flexible scale-up as workloads grow
End-to-end support for training, fine-tuning, inference

Recommended For:

Startups or research teams on a tight budget
Short-term AI projects needing powerful GPUs
Teams that want flexibility without building infrastructure

Option ②: Modular AI Datacenter (PMDC) with B200 Inside

ELICE provides PMDCs (Portable Modular Data Centers) equipped with B200 GPUs.

Why choose PMDC?

Fast setup: Operational in 3–4 months
Lower total cost: Up to 50% TCO savings
Scalable: Add GPU modules or blocks as needed

Recommended For:

Enterprises needing on-prem infrastructure
Public agencies with strict cloud policies
Organizations with long-term AI roadmaps

Want to Experience the B200 for Yourself?

We’ve prepared a special B200 promo to help you get started.

📩 Contact us for deployment consultation