A100 SXM4 80GB on Lambda Labs
Visit Lambda LabsLambda Labs offers the NVIDIA A100 SXM4 80GB, a top-tier enterprise GPU with 80GB HBM2e VRAM on the Ampere architecture, optimized for demanding AI, ML, and HPC workloads in data centers. This combination stands out for ML engineers seeking seamless, pre-configured environments without setup hassles. Lambda Labs, a premier GPU cloud provider and system integrator, delivers deep hardware expertise through its Lambda Stack—a curated software stack with CUDA, PyTorch, TensorFlow, and Jupyter pre-installed. Ideal for training large language models, fine-tuning transformers, or running inference on massive datasets, it supports multi-GPU scaling up to full clusters. Per-hour billing ensures cost efficiency for variable workloads, while reliable infrastructure minimizes downtime. Target audience: ML engineers prioritizing productivity over raw customization, valuing quick iteration from login to results. Key value propositions include hassle-free onboarding, expert-optimized hardware, and scalable performance for production-grade AI development.
Why NVIDIA A100 SXM4 80GB on Lambda Labs?
Choose Lambda Labs for the A100 SXM4 80GB due to their system integrator roots, ensuring superior hardware integration and reliability. Their Lambda Stack provides instant access to optimized ML frameworks, eliminating hours of dependency hell common elsewhere. Per-hour billing aligns perfectly with bursty training jobs on this high-VRAM GPU, avoiding overprovisioning costs. Lambda's expertise shines in multi-GPU configs, leveraging NVLink for efficient scaling on A100's Tensor Cores. Unlike generalist clouds, Lambda focuses on ML-specific optimizations like fast NVMe storage and high-bandwidth networking, complementing the A100's 19.5 TFLOPS FP64 and 312 TFLOPS TF32 performance for large-model training. This combo excels for teams needing production-ready environments without DevOps overhead.
Live Pricing
Real-time NVIDIA A100 SXM4 80GB offers from Lambda Labs
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Lambda Labs | 8×NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 240 vCPU 1800GB RAM 20000GB Storage | California | $1.79/GPU/hr $14.32/hr total (8×) | Sold Out | ||
Lambda Labs | 8×NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 240 vCPU 1800GB RAM 20480GB Storage | New South Wales | $2.79/GPU/hr $22.32/hr total (8×) | Sold Out | ||
Lambda Labs | 8×NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 240 vCPU 1800GB RAM 20480GB Storage | 🌍global | $2.79/GPU/hr $22.32/hr total (8×) | Sold Out | ||
Lambda Labs | 8×NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 240 vCPU 1800GB RAM 20480GB Storage | Virginia | $2.79/GPU/hr $22.32/hr total (8×) | Sold Out | ||
Lambda Labs | 8×NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 240 vCPU 1800GB RAM 20480GB Storage | Midwest | $2.79/GPU/hr $22.32/hr total (8×) | Sold Out |
Performance Notes
On Lambda Labs, the A100 SXM4 80GB delivers flagship Ampere performance: 312 TFLOPS TF32, 624 TFLOPS with sparsity, and 80GB HBM2e at 2 TB/s bandwidth, ideal for LLMs up to 175B parameters. Expect strong multi-GPU scaling via NVLink (up to 600 GB/s GPU-to-GPU) in 8x or larger clusters. Lambda provides 100-400 Gbps InfiniBand/RoCE networking and NVMe SSD storage (up to 30 TB local), enabling fast data loading. Benchmarks show near-linear scaling for PyTorch DDP training. Limitations: exact cluster interconnect details vary by instance size; public benchmarks confirm A100 excellence, but Lambda-specific perf may require user testing for custom 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
80GB
Architecture
Ampere
Tier
enterprise
Platform Features
Getting Started
Lambda Labs simplifies A100 SXM4 80GB access via a user-friendly dashboard. Sign up, launch pre-configured instances with Lambda Stack, and start training in minutes—no manual installs needed. Supports SSH, Jupyter, and TensorBoard out-of-the-box.
Steps
- 1Create a free Lambda Labs account and add payment for on-demand instances.
- 2Navigate to GPU Cloud dashboard, select '1x A100 SXM4 80GB' or multi-GPU config.
- 3Choose storage/OS (default Lambda Stack Ubuntu), set SSH key, and launch.
- 4Connect via SSH or JupyterLab link provided post-launch.
- 5Run 'nvidia-smi' to verify GPU, then pip install extras if needed.
Pro Tips
- Leverage Lambda Stack's pre-built Docker images for reproducible environments across spot/on-demand instances.
- For large models, start with 8x configs to exploit NVLink; monitor via Lambda's Prometheus/Grafana dashboard.
- Use spot instances for cost savings on non-urgent training, with auto-resume scripts for interruptions.
Frequently Asked Questions
What is Lambda Labs's billing model for NVIDIA A100 SXM4 80GB?▾
Lambda Labs bills per-hour for GPU instances including NVIDIA A100 SXM4 80GB. 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 A100 SXM4 80GB?▾
No, Lambda Labs does not currently offer spot instances for NVIDIA A100 SXM4 80GB. 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 A100 SXM4 80GB?▾
Yes, Lambda Labs offers reserved instance pricing for NVIDIA A100 SXM4 80GB, 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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