Key article takeaways
- GreenNode has officially added NVIDIA H200 Tensor Core GPU and NVIDIA RTX PRO 6000 Blackwell to its AI Cloud lineup.
- H200 is the first GPU equipped with 141GB of HBM3e memory at 4.8 TB/s bandwidth - nearly double the memory capacity and 1.4x the bandwidth of the H100 - making it ideal for training, HPC, and production of LLM workloads.
- RTX PRO 6000 Blackwell delivers 96GB of GDDR7 ECC memory (3x the memory of the RTX 5090) and optimized total cost of ownership (TCO), saving roughly 32% versus the H100 for inference and fine-tuning workloads.
GreenNode has officially added the two latest NVIDIA GPU lines, NVIDIA H200 Tensor Core GPU and NVIDIA RTX PRO 6000 Blackwell, to its AI Cloud lineup, giving enterprises more high-performance, cost-optimized infrastructure options to run modern AI workloads such as large language model (LLM) training, fine-tuning, large-scale inference, and high-performance computing (HPC).
Expanding the GPU lineup marks an important milestone in GreenNode's journey to build a hyperscale-class AI Cloud platform in Vietnam and Southeast Asia, serving the growing needs of AI startups, digital-transformation enterprises, and research institutes across industrieindustries, from finance and banking (BFSI), retail, and gaming to media.
Two next-generation GPUs added to GreenNode AI Cloud lineup
Previously, GreenNode's GPU infrastructure supported a range of widely adopted GPUs, including the NVIDIA H100 Tensor Core GPU, NVIDIA L40S, NVIDIA A40, and GeForce RTX 4090/5090. With the addition of the NVIDIA H200 and RTX PRO 6000 Blackwell, GreenNode now offers a broader GPU portfolio to meet diverse AI computing needs, from training and fine-tuning large-scale AI models, high-performance inference deployment, to graphics, simulation, and data-intensive computing. This expanded lineup enables customers to choose the optimal GPU configuration based on their workload requirements and budget.
GreenNode's core positioning for the two new GPU lines:
- NVIDIA H200 is the premium tier for training, HPC, and production LLLLM, a direct upgrade over the H100 with higher memory bandwidth, especially suited to memory-intensive workloads.
- NVIDIA RTX PRO 6000 Blackwell with a breakthrough 96GB of GDDR7 ECC memory, optimized for LLM inference, fine-tuning, and Omniverse/VFX graphics at an optimized cost.
NVIDIA H200: a leap in memory and bandwidth for LLM & HPC
Built on the NVIDIA Hopper architecture, the NVIDIA H200 is the first GPU to feature 141GB of HBM3e memory with 4.8 TB/s of bandwidtbandwidth, nearly double the memory capacity and 1.4x higher bandwidth than the NVIDIA H100. This is the decisive factor enabling the H200 to handle large language models with long context windows and HPC workloads that demand extremely high memory bandwidth.
Compared with the H100, the NVIDIA H200 delivers significant performance gains:
- Llama 2 70B Inference: up to 1.9x faster
- GPT-3 175B Inference: up to 1.6x faster
- High-Performance Computing: up to 110x faster than traditional x86 CPUs
Selected technical specifications of the H200 (SXM):
Specifications are provided for reference only and are subject to change. Tensor Core performance figures are measured with sparsity enabled.
People also read NVIDIA H100 vs H200: Key Differences in Performance, Specs, and AI Workloads for better understanding about the difference between these two Cloud GPUs.
NVIDIA RTX PRO 6000 Blackwell: 96GB GDDR7 ECC, optimized TCO
The NVIDIA RTX PRO 6000 Blackwell is built on the latest NVIDIA Blackwell architecture, delivering an impressive balance of AI performance, professional graphics capability, and optimized total cost of ownership. With 96GB of GDDR7 ECC memory, 24,064 CUDA cores, and 5th-generation Tensor Cores, it is an ideal choice for LLM inference, fine-tuning, and Omniverse/VFX graphics workloads.
Compared with the previous-generation RTX 6000 Ada, the RTX PRO 6000 Blackwell delivers:
- Model Iteration: 3x higher precision
- AI Training: 2.5x faster than the RTX 6000 Ada
- CFD Simulations: 4.5x faster
Selected key specifications:
RTX PRO 6000 Blackwell vs. existing options
| Key specification | GeForce RTX 5090 (Consumer Flagship) | RTX PRO 6000 Blackwell | NVIDIA H100 (Enterprise Datacenter) |
|---|---|---|---|
| Architecture | Blackwell | Blackwell PRO (Workstation/Server) | Hopper (Enterprise) |
| Memory capacity (VRAM) | 32GB GDDR7 | 96GB GDDR7 ECC (3x more) | 80GB HBM3 |
| Memory bandwidth | ~1.5 TB/s | 1.79 TB/s | 3.35 TB/s |
| Peak AI performance | ~1,600 TOPS (FP8) | 3,511 AI TOPS (new FP4 support) | ~4,000 TOPS (FP8) |
| GPU virtualization (MIG) | Not supported | Up to 4 instances | Up to 7 MIG instances |
| Total cost of ownership (TCO) | Low cost but no enterprise driver | Superior optimization (~32% savings vs. H100) | Very high upfront & operating cost |
The biggest differentiator of the RTX PRO 6000 Blackwell lies in the TCO equation: it offers 3x the memory of the GeForce RTX 5090 while including enterprise drivers and datacenter-grade stability, and it saves roughly 32% in cost compared with the H100 for inference and fine-tuning workloads.
GreenNode GPU lineup: the right GPU for every workload
GreenNode offers a complete GPU lineup so enterprises can select the right configuration for each stage and workload type:
| GPU Model | Positioning & recommended workloads |
|---|---|
| NVIDIA H200 (new) | Premium tier for training 100B+ parameter models, HPC, and long-context production LLM. 141GB HBM3e for long-context and multi-agent applications. |
| RTX PRO 6000 Blackwell (new) | LLM inference, fine-tuning of 13B–70B models, and Omniverse/VFX graphics with optimized TCO. 96GB GDDR7 ECC. |
For industries with strict security and compliance requirements such as finance and banking (BFSI), choosing the right GPU for each use case — from real-time fraud detection and video KYC to end-to-end financial auditing with long-context LLMs — is critical to balancing performance and cost.
GreenNode expands its AI Cloud portfolio for enterprise AI
GreenNode is designed to support workloads that demand high compute performance, strong networking, and system stability. Combined with a multi-AZ architecture spanning Hanoi, Ho Chi Minh City, and Bangkok, the two new GPU lines enable enterprises to build modern AI infrastructure within Vietnam with high availability and flexible scalability.
Common workloads include:
- AI / Machine Learning pipelines and large-model fine-tuning
- Large-scale inference systems and production LLMs
- Big Data analytics (Spark, ClickHouse, Presto)
- High-traffic backend services
- Recommendation systems and real-time analytics
The addition of the NVIDIA H200 and RTX PRO 6000 Blackwell further strengthens GreenNode's high-performance AI Cloud platform for enterprises, enabling organizations to deploy AI systems, data platforms, and mission-critical applications with high availability — while keeping infrastructure located in Vietnam and the region.
Contact the GreenNode team today for consultation on the optimal infrastructure architecture and the best-fit GPU configuration for your enterprise AI applications.
