GH200 Grace Hopper on Lambda Labs
Visit Lambda LabsLambda Labs, a premier GPU cloud provider renowned for its deep hardware expertise and role as a system integrator, offers the NVIDIA GH200 Grace Hopper Superchip—a 96GB VRAM Hopper architecture GPU in the enterprise tier. This combination is noteworthy for delivering unprecedented CPU-GPU coherence via 900 GB/s NVLink-C2C interconnect, pairing a 72-core NVIDIA Grace Arm CPU with the H100-equivalent Hopper GPU. Tailored for demanding HPC and AI workloads like large-scale model training and trillion-parameter inference, it targets ML engineers seeking pre-configured environments without setup hassles. Key value propositions include Lambda Stack—a battle-tested ML suite with CUDA, PyTorch, and TensorFlow pre-installed—per-hour billing for cost efficiency, and Lambda's hardware optimization ensuring peak performance. Ideal for teams prioritizing rapid prototyping to production-scale AI, this offering minimizes deployment friction while maximizing the GH200's groundbreaking memory bandwidth and compute density.
Why NVIDIA GH200 Grace Hopper on Lambda Labs?
Choose Lambda Labs for NVIDIA GH200 Grace Hopper due to their system integrator pedigree, which translates to finely tuned infrastructure that fully leverages the Superchip's NVLink-C2C integration for seamless CPU-GPU data flow. Lambda's pre-configured Lambda Stack eliminates hours of setup, providing optimized drivers, frameworks, and tools out-of-the-box—perfect for ML engineers focused on iteration over configuration. Per-hour billing offers flexibility for bursty workloads, contrasting commitment-heavy enterprise alternatives. Their deep hardware expertise ensures reliable scaling, robust support, and custom optimizations unavailable from generalist clouds, complementing GH200's enterprise-grade capabilities for trillion-parameter models and HPC simulations.
Live Pricing
Real-time NVIDIA GH200 Grace Hopper offers from Lambda Labs
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Lambda Labs | NVIDIA GH200 Grace Hopper 96GB VRAM | 96GB | 64 vCPU 432GB RAM 4096GB Storage | 🌍global | $2.29/GPU/hr | Sold Out | ||
Lambda Labs | NVIDIA GH200 Grace Hopper 96GB VRAM | 96GB | 64 vCPU 432GB RAM 4096GB Storage | Virginia | $2.29/GPU/hr | Sold Out |
Performance Notes
On Lambda Labs, expect GH200 to deliver Hopper-class performance with 96GB HBM3e VRAM enabling massive models without paging. The Grace CPU + GPU NVLink-C2C (900 GB/s bidirectional) shines for memory-intensive tasks like LLM fine-tuning. Network bandwidth likely includes high-speed InfiniBand (up to 400 Gb/s per reports), supporting efficient multi-node scaling, though exact configs are provider-specific. Storage options feature fast NVMe SSDs for datasets. Multi-GPU setups scale well via NVLink domains, but cluster-wide performance depends on Lambda's fabric—strong for AI but unbenchmarked publicly for GH200. Real-world TFLOPS and scaling await user reports; early indicators suggest 2-4x gains over H100 in coherent workloads.
A premier GPU cloud provider with deep hardware expertise, offering pre-configured environments for ML engineers.
Best For
Unique Features
- Lambda Stack for easy setup
- Deep hardware expertise as a system integrator
VRAM
96GB
Architecture
Hopper
Tier
enterprise
Platform Features
Getting Started
Getting started with NVIDIA GH200 on Lambda Labs is streamlined for ML engineers via their intuitive dashboard. Leverage pre-configured Lambda Stack instances for instant access to optimized ML environments, enabling quick launches of high-memory Hopper workloads without custom setup.
Steps
- 1Sign up for a Lambda Labs account and add payment details for on-demand access.
- 2Navigate to GPU Cloud dashboard and select GH200 Grace Hopper instance type.
- 3Configure specs: choose vCPU/RAM, storage (e.g., 1-4TB NVMe), and region.
- 4Launch instance; connect via SSH or Jupyter using provided credentials.
- 5Verify setup with 'nvidia-smi' and activate Lambda Stack if needed.
Pro Tips
- Use Lambda Stack's pre-installed PyTorch/CUDA for GH200-optimized training; avoids compatibility pitfalls common in raw Hopper deploys.
- Monitor utilization via Lambda's dashboard metrics to right-size instances and optimize per-hour costs for bursty AI jobs.
- For multi-node scaling, request NVLink/InfiniBand configs early—leverages GH200's interconnect for 90%+ scaling efficiency.
Frequently Asked Questions
What is Lambda Labs's billing model for NVIDIA GH200 Grace Hopper?▾
Lambda Labs bills per-hour for GPU instances including NVIDIA GH200 Grace Hopper. Hourly billing means you pay for full hours even if your job completes mid-hour. Plan your workloads accordingly to maximize cost efficiency.
Does Lambda Labs offer spot instances for NVIDIA GH200 Grace Hopper?▾
No, Lambda Labs does not currently offer spot instances for NVIDIA GH200 Grace Hopper. All instances are billed at on-demand rates. However, they do offer reserved instances for committed usage, which can provide significant discounts for long-term workloads.
Does Lambda Labs offer reserved instances for NVIDIA GH200 Grace Hopper?▾
Yes, Lambda Labs offers reserved instance pricing for NVIDIA GH200 Grace Hopper, which can provide significant discounts (typically 20-40% off on-demand rates) for committed usage periods. Reserved instances are ideal for predictable, long-running workloads like production inference services, ongoing training pipelines, or development environments that run continuously. Contact Lambda Labs for current reserved pricing and commitment terms.
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