Rent NVIDIA RTX Pro 6000 cloud GPU
96GB of GDDR7 VRAM on the Blackwell architecture, more memory than the H100, at a fraction of the cost. 21,760 CUDA cores, 5th-gen Tensor Cores with FP4 support, starting at just $1.55/hr on OpenRelay. The best value in cloud GPU compute for AI inference and training.
NVIDIA RTX Pro 6000: 96GB of Blackwell memory
The NVIDIA RTX Pro 6000 is the flagship professional GPU built on the Blackwell architecture, with 96GB of GDDR7 memory, 16GB more than an H100.
With 21,760 CUDA cores and 5th-generation Tensor Cores featuring native FP4 precision support, the Pro 6000 runs Llama 3.1 70B in 8-bit or Mixtral 8x22B in 4-bit on a single GPU.
At $1.55/hr on OpenRelay, the RTX Pro 6000 is the most cost-effective way to access 96GB of GPU memory in the cloud. That's 20% more VRAM than an H100. For teams running inference at scale, the math is simple: you get more memory, more flexibility, and dramatically lower costs. No other GPU in any cloud comes close to this value proposition.
RTX Pro 6000 technical specifications
| Specification | RTX Pro 6000 |
|---|---|
| GPU Architecture | Blackwell (GB202) |
| VRAM | 96GB GDDR7 |
| CUDA Cores | 21,760 |
| Memory Bandwidth | 1,792 GB/s |
| FP32 Performance | 105.1 TFLOPS |
| FP16 (Tensor) | 840 TFLOPS (with sparsity: 1,680) |
| FP4 (Tensor) | 3,352 TFLOPS (with sparsity: 6,704) |
| TDP | 350W |
| Memory Type | GDDR7 |
| Tensor Cores | 680 (5th Gen) |
| RT Cores | 170 (4th Gen) |
| FP4 Support | Yes (native Blackwell FP4) |
The Pro 6000's headline feature is its 96GB of GDDR7 memory, the largest VRAM capacity on any single GPU. While HBM-based GPUs like the H100 offer higher raw bandwidth, GDDR7 delivers massive capacity at a dramatically lower cost. For inference workloads where model size is the bottleneck (not bandwidth), the Pro 6000 is the optimal choice. The 5th-gen Tensor Cores also introduce native FP4 precision, enabling even higher throughput for quantized inference.
RTX Pro 6000 cloud GPU pricing on OpenRelay
At $1.55/hr, the RTX Pro 6000 on OpenRelay is the most cost-effective way to access 96GB of GPU memory in the cloud. Compare that to $3-5/hr for an H100 with only 80GB on hyperscalers.
| Provider | GPU | VRAM | $/hour | $/month |
|---|---|---|---|---|
| OpenRelay | RTX Pro 6000 | 96GB | $1.55 | $1,116 |
| AWS | H100 (80GB) | 80GB | $4.76 | $3,427 |
| Lambda Labs | H100 (80GB) | 80GB | $2.99 | $2,153 |
The numbers speak for themselves: 96GB of VRAM for $1.55/hr. That's $0.0161 per GB of VRAM per hour. All OpenRelay pricing includes storage, load balancing, and network egress with no hidden fees.
Best use cases for the RTX Pro 6000
The RTX Pro 6000 excels where massive VRAM capacity and cost-efficiency matter most. Its 96GB of memory opens up workloads that were previously only possible on expensive HBM GPUs:
Large Language Model Inference
Run Llama 3.1 70B in 8-bit, or serve multiple smaller models simultaneously. The 96GB VRAM eliminates the need for aggressive quantization that degrades output quality. With FP4 Tensor Core support, you can push throughput even further for latency-sensitive production deployments.
Cost-Effective Model Training & Fine-Tuning
Fine-tune 70B models with QLoRA, or smaller models with larger batch sizes, in 96GB of memory. The Pro 6000's 5th-gen Tensor Cores accelerate training with FP4 and FP8 precision, while the massive VRAM means fewer gradient checkpointing workarounds and faster time-to-convergence.
Multi-Modal & Vision-Language Models
Deploy large vision-language models like LLaVA-Next, InternVL2, and CogVLM2 that combine massive vision encoders with large language decoders. These models routinely exceed 48GB and benefit enormously from the Pro 6000's 96GB capacity. Process high-resolution images alongside long text contexts without running into memory walls.
Multi-Model Serving
Load multiple models into a single GPU simultaneously. With 96GB, you can serve a 7B LLM, an embedding model, and a reranker all on one Pro 6000, eliminating the need for multiple GPU instances. This dramatically reduces infrastructure costs for RAG pipelines, agent systems, and applications that chain multiple models together.
Diffusion Models & Image Generation
Run FLUX, Stable Diffusion 3, and other next-gen diffusion models at maximum resolution and batch size. The 96GB VRAM enables ultra-high-resolution generation (4K+), video generation models like Sora-class architectures, and large-batch rendering pipelines. Generate content at scale without memory constraints.
AI Research on a Budget
Experiment with new models and architectures without burning through your compute budget. At $1.55/hr, a day of continuous experimentation costs just $37.20, a fraction of what a day on a hyperscaler H100 ($72-120/day) would cost. Run the largest open-source models, test new quantization techniques, and iterate rapidly on research without cost anxiety.
How to deploy an RTX Pro 6000 on OpenRelay
Getting started with the RTX Pro 6000 on OpenRelay takes minutes, not days. No procurement process, no capacity reservations, no enterprise sales calls:
Create your account
Sign up at openrelay.inc/get-started, add a card, and make a deposit. No upfront commitment.
Select the RTX Pro 6000
Choose the RTX Pro 6000 from the GPU catalog. Configure your deployment with any Docker image, vLLM, TGI, Triton, ComfyUI, or your own custom container. The full 96GB VRAM is available to your workload.
Deploy with full GPU passthrough
OpenRelay provisions your Pro 6000 with VFIO passthrough for bare-metal performance. No GPU sharing, no virtualization overhead. The full 96GB GDDR7 and all 21,760 CUDA cores are exclusively yours.
Scale and optimize
Monitor GPU utilization, memory usage, and throughput in real-time. Scale up with more GPUs or scale down as needed, billing is per-minute so you never pay for idle compute. With auto-failover built in, your deployments stay online even if individual nodes go down.
RTX Pro 6000 vs H100: more VRAM, less cost
The RTX Pro 6000 and H100 serve different sweet spots. Here's how they compare head-to-head:
| Feature | RTX Pro 6000 | H100 SXM |
|---|---|---|
| VRAM | 96GB GDDR7 | 80GB HBM3 |
| Memory BW | 1,792 GB/s | 3,350 GB/s |
| CUDA Cores | 21,760 | 16,896 |
| FP32 | 105.1 TFLOPS | 67 TFLOPS |
| Architecture | Blackwell | Hopper |
| NVLink | No | Yes (900 GB/s) |
| TDP | 350W | 700W |
Choose the Pro 6000 when: You need maximum VRAM capacity for large model inference, you're optimizing for cost-per-token, or your workload is compute-bound rather than bandwidth-bound.
Choose the H100 when: You need NVLink for multi-GPU training, your workload is memory-bandwidth-bound (the H100's 3,350 GB/s HBM3 is nearly 2x faster), or you specifically need HBM for maximum sustained throughput. The H100 remains the gold standard for large-scale distributed training.
Frequently asked questions
How much does it cost to rent an RTX Pro 6000 on OpenRelay?
OpenRelay offers NVIDIA RTX Pro 6000 cloud GPUs at $1.55 per hour with per-minute billing. That works out to approximately $1,116 per month for 24/7 usage. There are no minimum commitments, no egress fees, and no hidden costs.
What is the RTX Pro 6000 and how does it differ from the RTX 6000 Ada?
The RTX Pro 6000 is NVIDIA's latest professional GPU built on the Blackwell architecture. It succeeds the RTX 6000 Ada Generation with significant upgrades: 96GB GDDR7 memory (vs 48GB GDDR6 on Ada), 21,760 CUDA cores, and 5th-generation Tensor Cores with FP4 support. The Blackwell architecture delivers a massive leap in AI inference performance over the previous Ada Lovelace generation.
How does the RTX Pro 6000 compare to the H100 for inference?
The RTX Pro 6000 offers 96GB of GDDR7 VRAM, more than the H100's 80GB HBM3. While the H100's HBM3 provides higher memory bandwidth (3,350 GB/s vs ~1,792 GB/s), the Pro 6000's larger memory capacity means it can run models that don't fit on the H100 without quantization.
What AI models can run on the RTX Pro 6000?
With 96GB of VRAM, the RTX Pro 6000 fits Llama 3.1 70B in 8-bit, Mixtral 8x22B in 4-bit, large vision-language models, or several smaller models side by side on one GPU.
Is the RTX Pro 6000 good for AI training?
Yes, the RTX Pro 6000 is excellent for training. Its 96GB VRAM allows larger batch sizes and bigger models than most other GPUs. The 5th-generation Tensor Cores with FP4 support accelerate training significantly. While it lacks NVLink for multi-GPU communication (unlike the H100), it's an outstanding choice for single-GPU or data-parallel training workloads where the massive memory capacity is the priority.
Why is the RTX Pro 6000 so cheap on OpenRelay?
OpenRelay sources GPUs from a distributed network of providers, which eliminates the massive overhead of hyperscaler data centers. The RTX Pro 6000 at $1.55/hr reflects our commitment to making high-end GPU compute accessible. There's no markup for enterprise sales teams, no egress fees, and no minimum commitments. You get bare-metal GPU performance with VFIO passthrough at a fraction of what traditional cloud providers charge.
Ready to deploy on the RTX Pro 6000?
96GB of VRAM at $1.55/hr, the best value in cloud GPU compute. No minimum commitments, no egress fees.