Rent RTX Pro 6000 Cloud — 96GB Blackwell GPU at $1.95/hr
RTX Pro 6000 Blackwell instances with 96GB of GDDR7 memory. Bare-metal SSH access with full root, NVMe storage. Built for AI researchers and engineers who need GPU power without enterprise overhead.
$1.95/hour — View pricing
Why Rent an RTX Pro 6000?
Hardware Specifications
- GPU: RTX Pro 6000 Workstation Edition — Blackwell Architecture
- VRAM: 96GB GDDR7 with ECC — 1.8 TB/s across a 512-bit bus
- Storage: 2 TB NVMe — Ephemeral or Persistent
- Access: Bare-metal SSH — full root, Docker optional
96GB of Dedicated GDDR7
Serve a 70B-class model quantized to 4-bit or 8-bit on a single GPU, fine-tune mid-size models with LoRA or QLoRA, or hold several smaller models resident at once for multi-agent workflows — without sharding across nodes.
125 TFLOPS of FP32
188 streaming multiprocessors and 752 5th-generation Tensor Cores deliver 125 TFLOPS of FP32 — close to double an H100's 67 TFLOPS — plus 4,000 AI TOPS of FP4 (with sparsity), a precision the Hopper generation does not support at all.
Blackwell Architecture
Develop on the same architecture you'll deploy to production. Code written for Blackwell uses features — 5th-gen Tensor Cores and improved sparsity support — that do not exist on Hopper.
Real GDDR7 Bandwidth
A dedicated 512-bit GDDR7 memory subsystem keeps decode-bound inference fed, rather than throttling on the shared low-bandwidth memory found in small unified-memory devices.
Who Is Enverge RTX Pro 6000 Cloud For?
The dividing line is whether the work fits on a single GPU. It does not have NVLink, so it is strongest at serving and single-card tuning rather than distributed training.
Built for you if...
- Production LLM Inference — Where this card is strongest. For models that fit in 96GB, it matches or beats an H100 on single-GPU throughput, at a fraction of the hourly rate.
- High-Concurrency Serving — Native FP4 and real GDDR7 bandwidth hold throughput up as concurrent requests climb.
- Fine-Tuning Without a Cluster — LoRA and QLoRA on models up to 70B, or a full fine-tune at 13B–34B, on one card.
- Multi-Agent Systems — 96GB holds a reasoning model, a coder, and an embedding model resident at once.
Not the best fit if...
- Frontier-Scale Distributed Training — No NVLink; GPUs talk over PCIe. If your model only fits across four or more GPUs, NVLink hardware is worth the premium.
- FP64 & HPC Simulation — Tuned for FP4, FP8, and FP16; double-precision throughput is cut.
- Simple API Wrappers — If you are just calling OpenAI or Anthropic APIs, you do not need dedicated GPU hardware.
Enverge GPU Range — Price Comparison
Every card here is available from Enverge; rates mirror the pricing table on enverge.ai. DGX Spark carries more memory capacity for less money than the RTX Pro 6000, but at roughly a sixth of the bandwidth. Capacity gets a model resident; bandwidth determines how fast it runs.
| GPU | Memory | Bandwidth | Hourly | Monthly |
|---|
| NVIDIA DGX Spark | 128GB unified | 273 GB/s | $0.75 | ~$548 |
| RTX Pro 6000 | 96GB GDDR7 | 1.8 TB/s | $1.95 | ~$1,424 |
| NVIDIA H100 | 80GB HBM3 | 3.35 TB/s | $4.00 | ~$2,920 |
| NVIDIA H200 | 141GB HBM3e | 4.8 TB/s | $5.50 | ~$4,015 |
| NVIDIA B300 | 288GB HBM3e | 8 TB/s | $7.50 | ~$5,475 |
Monthly figures are the on-demand hourly rate × 730h, before any reserved-capacity discount. DGX Spark memory is unified CPU+GPU.
Enverge RTX Pro 6000 Cloud Pricing
Pay-per-hour, no commitment. Billed daily for actual runtime.
RTX Pro 6000 — $1.95/hour
Single GPU, Workstation Edition, 96 GB GDDR7 at 1.8 TB/s.
- Full Root Access
- Founder Support (during beta)
- Bare-metal SSH
- Docker (optional)
- NVMe Storage
Frequently Asked Questions — Renting an RTX Pro 6000 in the Cloud
What is Enverge RTX Pro 6000 Cloud?
Enverge RTX Pro 6000 Cloud gives you remote SSH access to a dedicated RTX Pro 6000 Blackwell GPU with 96GB of GDDR7 memory. SSH is the primary path: you get bare-metal performance on the machine itself, with NVMe storage and a full CUDA toolchain, without buying the hardware. Docker is available on top if you prefer to work in containers.
How much does it cost to rent an RTX Pro 6000?
Pricing is pay-per-hour with no commitment: $1.95/hour for a single RTX Pro 6000 Workstation Edition with 96GB of GDDR7 at 1.8 TB/s. That includes bare-metal SSH with full root, NVMe storage, and founder support during beta. You are billed daily for actual runtime.
What is the difference between the RTX Pro 6000 and an H100?
The RTX Pro 6000 is a Blackwell-generation GPU with 96GB of GDDR7 at 1.8 TB/s and 5th-generation Tensor Cores, including native FP4 support that Hopper does not have at all. The H100 is the previous generation with 80GB of HBM3. The RTX Pro 6000 gives you more memory capacity, roughly double the FP32 throughput, and Blackwell-only precisions, while the H100's HBM3 still leads on raw bandwidth and FP8 throughput for memory-bound training — and the RTX Pro 6000 rents for a fraction of typical H100 hourly rates.
Who is Enverge RTX Pro 6000 Cloud for?
Teams serving LLMs in production, engineers fine-tuning on a single card with LoRA or QLoRA, and researchers building multi-agent systems on Blackwell-class hardware — without committing to enterprise hardware purchases or long-term cloud contracts.
How do I access the RTX Pro 6000?
SSH straight into your dedicated instance and work on bare metal with full root — that is the main path, and the CUDA toolkit plus the NVIDIA AI stack are ready to use. Docker is there if you want containers, but nothing requires it. Connect from any terminal.
Can I run large language models on an RTX Pro 6000?
Yes. 96GB of GDDR7 is enough to serve a 70B-class model quantized to 4-bit or 8-bit on a single GPU, to fine-tune mid-size models with LoRA or QLoRA, or to hold several smaller models in memory at once for multi-agent workflows.
Is this the same as NVIDIA DGX Cloud?
No. NVIDIA DGX Cloud is an enterprise platform with multi-node clusters for large-scale training. Enverge gives you a single dedicated RTX Pro 6000 GPU — ideal for individual researchers and small teams at a fraction of the cost.
What software is pre-installed?
Each instance comes with Ubuntu, the CUDA toolkit, cuDNN, a current NVIDIA driver, Docker, Python 3, and PyTorch. You have full root access to install anything else.