Why Rent a DGX Spark?
Hardware Specifications
- GPU: NVIDIA GB10 — Blackwell Architecture
- VRAM: 128GB Unified Memory — 273 GB/s Bandwidth
- Storage: 1 TB NVMe — Ephemeral or Persistent
- Access: SSH + Docker — Full Root Control
128GB Unified Memory
Run multi-agent systems or 200B parameter models without sharding. Load a 120B reasoning model, a 6.7B coder, and a 4B embedding model simultaneously — all in memory without swapping.
Native NVFP4 Quantization
Combine NVFP4 with Speculative Decoding for 3x faster inference while maintaining accuracy. Both draft and target models fit in 128GB unified memory.
Blackwell Architecture (SM 10.0)
Develop on the same architecture you'll deploy to production. Code written for Blackwell uses SM 10.0 features — 5th-gen Tensor Cores and improved sparsity support — that don't exist on Hopper's SM 9.0.
Transformer Engine 2.0
Second-generation attention acceleration delivers 2x attention speedup. Measure actual inference throughput on your models before committing to hardware.
Frequently Asked Questions — Renting NVIDIA DGX Spark in the Cloud
What is DGX Spark Cloud?
DGX Spark Cloud gives you remote SSH access to a dedicated NVIDIA DGX Spark workstation — featuring the GB10 Blackwell GPU with 128GB unified memory. You get bare-metal performance with Docker support, NVMe storage, and full CUDA 12.8 compatibility, without buying the hardware.
How much does it cost to rent a DGX Spark?
Pay-as-you-go starts at $0.55/hour. The Unlimited plan is $350/month (~$0.48/hour) and includes 24/7 dedicated access with full root control and persistent NVMe storage. No hidden fees.
What is the difference between DGX Spark and H100?
DGX Spark uses the Blackwell GB10 GPU (SM 10.0) with 128GB unified memory at 273 GB/s bandwidth. The H100 uses Hopper architecture (SM 9.0) with 80GB HBM3. DGX Spark offers 60% more memory, native NVFP4 quantization, and 2nd-generation Transformer Engine — at roughly 6x lower monthly cost.
Who is DGX Spark Cloud for?
AI researchers, ML engineers, and teams who need to train models, fine-tune LLMs, build multi-agent systems, or benchmark on Blackwell architecture — without committing to enterprise hardware purchases or long-term cloud contracts.
How do I access the DGX Spark?
You get direct SSH access to your dedicated DGX Spark instance. Docker is pre-installed, CUDA 12.8 and the full NVIDIA AI stack are ready to use. Connect from any terminal.
Can I run large language models on DGX Spark?
Yes. With 128GB unified memory, DGX Spark can run models up to 200B parameters without sharding. You can load multiple models simultaneously 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. DGX Spark Cloud by Enverge gives you a single dedicated DGX Spark workstation — ideal for individual researchers and small teams at a fraction of the cost.
What software is pre-installed?
Each instance comes with Ubuntu, CUDA 12.8, cuDNN, NVIDIA driver 535+, Docker, Python 3, PyTorch, and the full NVIDIA AI Enterprise stack. You have root access (Unlimited plan) to install anything else.