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Cloud GPUs
NVIDIA RTX PRO 6000 cloud GPUs.
Plan larger foundational model workloads on RTX PRO 6000-class infrastructure with 96GB VRAM, ECC memory, MIG support, and enterprise-grade deployment paths on Hivenet.
A higher-capacity GPU tier for larger AI workloads.
RTX PRO 6000 capacity expands the Hivenet GPU fleet for workloads that need more memory, stronger isolation, and enterprise deployment options. Use RTX 4090 or RTX 5090 when the workload fits those tiers. Use RTX PRO 6000 when memory, isolation, or deployment control changes the decision.
Memory
96GB VRAM
Larger VRAM gives 70B-class and larger quantized model workloads more room before multi-GPU complexity becomes necessary.
Isolation
MIG support
Supported MIG configurations can help separate workloads or tenants with stronger GPU-level partitioning.
Reliability
ECC memory
Server-class memory support helps workloads where stability and operational confidence matter.
Security path
Confidential-computing potential
Hardware-supported confidential-computing capabilities can support more secure deployment paths where available and approved.
RTX PRO 6000 specs
Spec
Value
Why it matters
Architecture
NVIDIA Blackwell
Built for current AI inference, compute, and accelerated workload classes
Memory
96GB GDDR7 ECC
Supports larger foundational models, longer context, and enterprise reliability needs
Memory bandwidth
1,597 GB/s
Helps sustain data movement for large-context and batch inference workloads
Inference formats
FP8 / FP4 support
Useful for throughput-oriented serving when the model and stack support lower precision
MIG support
Yes
Enables GPU partitioning for isolated workloads where supported
Confidential Computing
Supported where available
Can help with sensitive workload paths that need hardware-backed isolation and attestation
PCIe interface
Gen 5 ×16
Higher-bandwidth server connection for modern GPU systems
TDP
Up to 600 W, configurable
Important for enterprise server design, capacity planning, and cooling profile
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Popular use cases
Run 70B-class foundational model inference
Use the 96GB VRAM class for larger model workloads where a single-GPU path can simplify serving, testing, and deployment planning.
Build enterprise RAG systems
Pair larger model capacity with S3-compatible storage, retrieval pipelines, and managed or self-managed inference for document-heavy AI workflows.
Serve isolated AI workloads
Use MIG-supported deployment paths where workload separation, tenant isolation, or dedicated inference slices matter.
Plan private AI deployments
Scope AI systems for sensitive or business-critical data with GPU capacity, regional planning, and guided infrastructure support.
Run larger vision and multimodal workloads
Use more memory headroom for workloads that combine text, image, document, video, or long-context inputs.
Fine-tune and adapt larger models
Use larger GPU memory for selected fine-tuning, adaptation, and evaluation workflows that exceed smaller GPU limits.
RTX 4090, RTX 5090, or RTX PRO 6000?
The best GPU depends on the workload. Hivenet's GPU fleet is designed to match model size, memory needs, throughput, and cost-performance instead of pushing every workload to the largest card.
RTX 4090
Testing, research, notebooks, image generation, smaller model workloads, and cost-efficient GPU work
RTX 5090
Higher-throughput inference, demanding AI workloads, creative workloads, and stronger single-GPU performance
RTX PRO 6000
Larger foundational models, 70B-class inference, enterprise RAG, multi-tenant inference, and private AI workloads
Planning a larger foundational model workload?
Tell us what you want to run. Hivenet will help you choose the right path across RTX 4090, RTX 5090, RTX PRO 6000 capacity, Inference API, and Private AI.
