Accelerate AI training, LLM inference, and high-performance computing with dedicated NVIDIA H200 GPU servers from MIG Servers. Powered by NVIDIA Hopper architecture and 141GB of HBM3e memory, our 100% bare-metal infrastructure delivers exceptional memory bandwidth, scalability, and performance for enterprise AI and data-intensive workloads.
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Maximize the 4.8 TB/s memory bandwidth of the NVIDIA H200 with dedicated bare-metal infrastructure and no virtualization overhead. MIG Servers provides high-performance infrastructure designed for demanding Generative AI, LLM inference, AI training, and HPC workloads.
Zero noisy neighbors or virtualization overhead.
Dedicated access to the H200's 141GB HBM3e memory resources.
Full root control for custom AI software stacks.
Hosted in fault-tolerant enterprise data center facilities.
Advanced cooling systems built for high-density GPU racks.
Backed by a 99.99% uptime SLA.
Low-latency connectivity through premium Tier 1 network providers.
High-speed bandwidth for massive dataset ingestion.
Optimized for multi-node clustering and fast data transfer.
Predictable, flat-rate pricing for dedicated instances.
Avoid variable consumption-based pricing associated with many cloud platforms.
Well suited for long-running AI training and inference workloads.
Direct access to in-house server architects and hardware engineers.
Proactive monitoring and rapid hardware replacement.
Round-the-clock support for mission-critical infrastructure.
Physically isolated hardware for your proprietary data.
Secure environment for deploying sensitive Generative AI models.
Meets strict enterprise compliance and data protection standards.
Powered by NVIDIA Hopper™ architecture, the H200 GPU is designed to accelerate Large Language Models (LLMs), AI inference, AI training, and High-Performance Computing (HPC) workloads with high-capacity HBM3e memory and high-bandwidth GPU interconnects.
Built on NVIDIA Hopper architecture, the H200 utilizes advanced Tensor Cores to handle massive amounts of data. It delivers high throughput for Generative AI, advanced Tensor Core performance for AI training and inference workloads, ensuring high efficiency for deep learning training and inference.
The industry's first GPU featuring 141GB of HBM3e memory. To maximize hardware utilization across development teams, Multi-Instance GPU (MIG) technology securely partitions a single H200 into up to seven fully isolated instances (up to 7 isolated GPU instances). Each partition operates with its own dedicated compute, cache, and memory bandwidth, ideal for diverse LLM workloads.
Available in robust configurations to suit your data center needs. The SXM form factor is designed for HGX clusters with 4 or 8 GPUs delivering high-performance scaling across multiple GPUs. The PCIe-based H200 NVL is optimized for lower-power, air-cooled enterprise racks, utilizing a 2- or 4-way NVIDIA NVLink bridge (900GB/s per GPU) for seamless multi-GPU acceleration.
Designed to support demanding AI and HPC workloads with enhanced memory capacity and bandwidth, the NVIDIA H200 delivers significant performance improvements for modern data-intensive applications. With 141GB of HBM3e memory and up to 4.8 TB/s memory bandwidth, organizations can process larger models and datasets more efficiently.
Secure your proprietary AI models, algorithms, and highly sensitive datasets in use. The H200 fully supports NVIDIA Confidential Computing, providing hardware-based security that isolates and protects your workloads from unauthorized access through hardware-based isolation mechanisms.
Leverage the 141GB HBM3e memory and up to 4.8 TB/s memory bandwidth of the NVIDIA H200 for AI, HPC, and data-intensive workloads.
Leverage the NVIDIA H200 for large language model inference, Generative AI, and enterprise Retrieval-Augmented Generation (RAG). Dedicated bare-metal resources help deliver consistent performance for complex foundation models and memory-intensive AI workloads.
Accelerate scientific computing, molecular dynamics, climate modeling, and other memory-intensive HPC workloads with up to 4.8 TB/s memory bandwidth. Enterprise-grade infrastructure and cooling systems are designed to support long-running computational workloads.
Process large-scale datasets for analytics, machine learning, and ETL workflows using advanced Tensor Core acceleration. Sensitive enterprise workloads benefit from dedicated hardware resources and NVIDIA Confidential Computing security features.
Compare the NVIDIA H200 and H100 GPUs across memory capacity, bandwidth, and AI performance metrics, and explore the operational advantages of dedicated bare-metal infrastructure.
| Feature | NVIDIA H200 GPU | NVIDIA H100 GPU |
|---|---|---|
| GPU Memory | 141GB HBM3e | 80GB HBM3 |
| Memory Bandwidth | 4.8 TB/s | 3.35 TB/s |
| LLM Inference (Llama2 70B) | Up to 1.9X Faster | Baseline (1X) |
| LLM Inference (GPT-3 175B) | Up to 1.6X Faster | Baseline (1X) |
The H200 significantly increases available GPU memory and memory bandwidth compared to the H100, making it well suited for larger AI models, inference workloads, and memory-intensive HPC applications.
| Feature | MIG Servers (Bare-Metal H200) | Typical Public Cloud VMs |
|---|---|---|
| Hardware Access |
100% Dedicated(Direct Access) |
Virtualized(Hypervisor Overhead) |
| Performance |
Consistent Peak Throughput |
Unpredictable(Noisy Neighbors) |
| Data Security |
Physically Isolated Compute |
Shared Multi-TenantShared Environment |
| TCO & Pricing |
Predictable Flat-RateMonthly Fixed Pricing |
Variable Usage& High Egress Fees |
Performance figures are based on NVIDIA published benchmark data and may vary depending on workload configuration, software stack, model architecture, and deployment environment.
Deploy AI, machine learning, and inference workloads on dedicated NVIDIA H200 infrastructure with support for industry-standard frameworks, container platforms, and enterprise AI software.
MIG Servers provides the bare-metal foundation required to run PyTorch, TensorFlow, and the NVIDIA CUDA toolkit without virtualization overhead. Well suited for teams building LLM training, inference, and machine learning workflows on NVIDIA H200 GPU servers.
Configure your dedicated hardware exactly how your team needs it. We support enterprise-grade operating systems including Ubuntu and enterprise Linux distributions, fully compatible with containerized Docker environments and Kubernetes orchestration platforms.
Deploy Generative AI, computer vision, speech AI, and Retrieval-Augmented Generation (RAG) applications using NVIDIA AI Enterprise and NVIDIA NIM microservices. Accelerate development and deployment workflows with software designed for enterprise AI environments.
Yes. We provide 100% bare-metal, single-tenant NVIDIA H200 servers with exclusive root access and dedicated hardware resources. Unlike shared environments, your workloads run on dedicated infrastructure designed for consistent performance, resource isolation, and full administrative control.
The NVIDIA H200 features 141GB of HBM3e memory and up to 4.8 TB/s memory bandwidth, compared to 80GB and 3.35 TB/s on the NVIDIA H100. The increased memory capacity and bandwidth help support larger AI models, larger context windows, and memory-intensive inference workloads. NVIDIA benchmark data also indicates performance improvements for selected LLM inference workloads.
Yes. NVIDIA H200 supports Multi-Instance GPU (MIG) technology, allowing a single GPU to be divided into up to seven isolated instances. Each instance receives dedicated compute, memory, and cache resources, enabling multiple users or workloads to operate independently on the same GPU.
NVIDIA H200 dedicated servers are compatible with leading AI and machine learning frameworks, including CUDA, PyTorch, TensorFlow, Docker, Kubernetes, NVIDIA AI Enterprise, and NVIDIA NIM microservices. This enables teams to build, train, deploy, and scale AI applications using industry-standard tools.
We support a wide range of operating systems, including Ubuntu and other Linux distributions, Windows Server 2016, Windows Server 2019, Windows Server 2022, and Windows Server 2025. Custom operating system installations may also be available upon request, allowing teams to deploy environments tailored to their application and infrastructure requirements.
NVIDIA H200 servers are designed for Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), machine learning, deep learning, scientific simulations, data analytics, computer vision, speech AI, and other GPU-accelerated workloads.
Our NVIDIA H200 dedicated servers currently include a 10 Gbps network connection with 10 TB monthly bandwidth allocation. Additional networking options and higher-capacity configurations may be available based on deployment requirements. Please contact our team to discuss custom networking solutions for AI training, inference, and HPC workloads.
Power your AI training, LLM inference, scientific computing, and data-intensive applications with dedicated NVIDIA H200 GPU servers from MIG Servers. Gain direct access to 141GB of HBM3e memory, dedicated hardware resources, and a bare-metal environment designed for high-performance computing workloads.
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