H100 SXM5 on Lambda Labs
Visit Lambda LabsLambda Labs offers the NVIDIA H100 SXM5 GPU with 80GB HBM3 VRAM on the Hopper architecture, a top-tier enterprise solution for AI training, inference, and HPC workloads. This GPU delivers exceptional performance, including up to 4 petaFLOPS in FP8 Tensor Core operations and advanced features like the Transformer Engine for optimized large language models and diffusion models. What makes this combination noteworthy is Lambda Labs' position as a premier GPU cloud provider with deep hardware expertise as a system integrator. They provide pre-configured environments via the Lambda Stack, which includes the latest CUDA, cuDNN, PyTorch, TensorFlow, and JAX, enabling ML engineers to bypass complex setups and focus on model development. Targeted at ML engineers seeking production-ready infrastructure, key value propositions include per-hour billing for cost flexibility, rapid instance provisioning, seamless multi-GPU scaling, and expert support. This setup is ideal for iterative experimentation, fine-tuning LLMs, or scaling distributed training without vendor lock-in or setup friction.
Why NVIDIA H100 SXM5 on Lambda Labs?
Lambda Labs pairs perfectly with the H100 SXM5 due to their hardware integration expertise, ensuring clusters are optimized for Hopper GPUs with high-bandwidth NVLink (up to 900GB/s bidirectional) and InfiniBand interconnects for efficient multi-node scaling. The Lambda Stack provides a pre-tuned ML environment with NVIDIA drivers, frameworks, and libraries, reducing setup time from hours to minutes—crucial for the H100's demanding workloads like trillion-parameter models. Per-hour billing suits variable ML pipelines, offering pay-as-you-go without long-term commitments. Lambda's ML-focused interface, fast provisioning (under 5 minutes), and responsive support complement the H100's 80GB VRAM and FP8 capabilities, enabling cost-effective training of massive models. Unlike general clouds, Lambda prioritizes deep learning optimizations, making it a go-to for engineers prioritizing velocity and reliability.
Live Pricing
Real-time NVIDIA H100 SXM5 offers from Lambda Labs
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Lambda Labs | 8×NVIDIA H100 SXM5 80GB VRAM | 80GB | 208 vCPU 1800GB RAM 20000GB Storage | California | $3.49/GPU/hr $27.92/hr total (8×) | Sold Out | ||
Lambda Labs | 8×NVIDIA H100 SXM5 80GB VRAM | 80GB | 208 vCPU 1800GB RAM 20000GB Storage | Virginia | $3.49/GPU/hr $27.92/hr total (8×) | Sold Out | ||
Lambda Labs | 8×NVIDIA H100 SXM5 80GB VRAM | 80GB | 208 vCPU 1800GB RAM 22528GB Storage | Texas | $3.99/GPU/hr $31.92/hr total (8×) | Sold Out | ||
Lambda Labs | 8×NVIDIA H100 SXM5 80GB VRAM | 80GB | 208 vCPU 1800GB RAM 22528GB Storage | United States | $3.99/GPU/hr $31.92/hr total (8×) | Sold Out | ||
Lambda Labs | 8×NVIDIA H100 SXM5 80GB VRAM | 80GB | 208 vCPU 1800GB RAM 22528GB Storage | Texas | $3.99/GPU/hr $31.92/hr total (8×) | Sold Out |
Performance Notes
Lambda Labs' H100 SXM5 instances deliver near-peak Hopper performance: ~4 PFLOPS FP8, ~2 PFLOPS TF32/FP16 for AI, with 80GB HBM3 VRAM suiting large-batch training and long-context inference. Expect strong multi-GPU scaling via NVLink for 8-GPU nodes and RoCE/InfiniBand (400-800Gbps) for clusters, supporting PyTorch DDP, FSDP, and Megatron efficiently. Storage includes high-IOPS NVMe SSDs (up to 30TB+), with NFS for datasets. Lambda's optimizations yield MLPerf-competitive results, but exact benchmarks vary by config—check their docs or run NCCL tests. Limitations: network details instance-specific; CPU/RAM ratios (e.g., dual AMD EPYC) solid but not A100-overkill. Overall, reliable for production ML with low overhead.
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
80GB
Architecture
Hopper
Tier
enterprise
Platform Features
Getting Started
Launching NVIDIA H100 SXM5 on Lambda Labs is designed for speed and simplicity. With a free account signup, intuitive dashboard, and Lambda Stack pre-installed, ML engineers can provision GPU instances in minutes. Access via SSH, JupyterLab, or TensorBoard for immediate training, scaling from single-GPU prototyping to multi-node clusters.
Steps
- 1Sign up at lambdalabs.com, verify email, and add a payment method for per-hour billing.
- 2Go to GPU Cloud dashboard, filter for H100 SXM5, and select instance size (1-8 GPUs).
- 3Configure storage (NVMe SSD), OS (Lambda Stack Ubuntu), and optional autoscaling.
- 4Click 'Launch'—instance ready in 2-5 minutes with public IP and SSH key.
- 5Connect via SSH (ssh user@ip) or browser-based JupyterLab for GPU workloads.
Pro Tips
- Leverage Lambda Stack's pre-built PyTorch 2.1+ with Hopper CUDA 12.x—test multi-GPU with torch.distributed.launch immediately.
- For cost savings, use spot instances if available and snapshot volumes before shutdown to resume training fast.
- Optimize H100 with FP8/Transformer Engine via NVIDIA docs; monitor utilization with nvidia-smi and Lambda's dashboard.
Frequently Asked Questions
What is Lambda Labs's billing model for NVIDIA H100 SXM5?▾
Lambda Labs bills per-hour for GPU instances including NVIDIA H100 SXM5. 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 H100 SXM5?▾
No, Lambda Labs does not currently offer spot instances for NVIDIA H100 SXM5. 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 H100 SXM5?▾
Yes, Lambda Labs offers reserved instance pricing for NVIDIA H100 SXM5, 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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