A100 PCIe 80GB on Paperspace
Visit PaperspacePaperspace's NVIDIA A100 PCIe 80GB offering delivers enterprise-grade AI acceleration through its Gradient MLOps platform, tailored for individual developers, educators, and small teams. This 80GB VRAM Ampere GPU excels in training and inference for large language models, data analytics, and HPC workloads, supporting up to 19.5 TFLOPS FP64 and 312 TFLOPS Tensor FP16 performance. What sets this combination apart is Paperspace's seamless notebook-to-deployment workflow via Gradient, enabling rapid experimentation without infrastructure management. Per-second billing minimizes costs for bursty workloads, ideal for prototyping massive models like GPT variants or fine-tuning on datasets exceeding 40GB. Target users—solo ML engineers and academic researchers—benefit from JupyterLab integration, pre-configured environments (PyTorch, TensorFlow), and collaborative features. While not optimized for massive clusters, it provides accessible high-memory compute, bridging the gap between local GPUs and cloud-scale HPC. Limitations include PCIe interconnects limiting multi-GPU scaling compared to SXM variants, but single-GPU efficiency shines for memory-bound tasks.
Why NVIDIA A100 PCIe 80GB on Paperspace?
Choose Paperspace for the NVIDIA A100 PCIe 80GB if you prioritize developer-friendly ML workflows over raw cluster scale. Gradient's unified platform streamlines notebooks, experiments, and deployments, complementing the GPU's 80GB HBM2e memory for handling enormous models without data sharding. Per-second billing offers flexibility for intermittent use, reducing costs versus hourly models—pause instances during idle periods. Paperspace's strengths in ease-of-use, with one-click setups for CUDA 12+, cuDNN, and ML frameworks, lower the barrier for individuals versus complex providers like AWS or GCP. Fast NVMe storage (up to 2TB) and 100Gbps networking support efficient data loading. This combo uniquely empowers education and solo devs with enterprise hardware, avoiding steep learning curves while delivering cost-effective access to Ampere's Multi-Instance GPU (MIG) for workload partitioning.
Live Pricing
Real-time NVIDIA A100 PCIe 80GB offers from Paperspace
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Paperspace | NVIDIA A100 PCIe 80GB 80GB VRAM | 80GB | 12 vCPU 90GB RAM 50GB Storage | New York | $3.18/GPU/hr | Available | ||
Paperspace | 8×NVIDIA A100 PCIe 80GB 80GB VRAM | 80GB | 96 vCPU 720GB RAM 50GB Storage | New York | $3.18/GPU/hr $25.44/hr total (8×) | Available | ||
Paperspace | 8×NVIDIA A100 PCIe 80GB 80GB VRAM | 80GB | 96 vCPU 720GB RAM 50GB Storage | Amsterdam | $3.18/GPU/hr $25.44/hr total (8×) | Sold Out | ||
Paperspace | 2×NVIDIA A100 PCIe 80GB 80GB VRAM | 80GB | 24 vCPU 180GB RAM 50GB Storage | Amsterdam | $3.18/GPU/hr $6.36/hr total (2×) | Sold Out | ||
Paperspace | 4×NVIDIA A100 PCIe 80GB 80GB VRAM | 80GB | 48 vCPU 360GB RAM 50GB Storage | Canada | $3.18/GPU/hr $12.72/hr total (4×) | Sold Out |
Performance Notes
On Paperspace, the A100 PCIe 80GB delivers near-native performance for single-GPU tasks, with 80GB VRAM enabling full-model loading for transformers up to 70B parameters. Expect strong throughput in FP16/bfloat16 training (up to 300+ TFLOPS Tensor Core) and MIG partitioning into 7x10GB instances for concurrent jobs. PCIe 4.0 x16 limits host interconnect to ~32GB/s bidirectional, suitable for solo use but suboptimal for NVLink-scale multi-GPU (Paperspace supports up to 8x A100 clusters via Gradient, though PCIe latency adds ~10-20% overhead vs. DGX). Storage includes 2TB NVMe SSD with 5GB/s reads; networking at 100Gbps Ethernet aids data transfer. Benchmarks show 1.5-2x speedup over V100 for BERT training; real-world variability depends on host CPU (AMD EPYC) and software stacks. Multi-GPU scaling is functional but not leader-class—test for your workload.
A provider offering the Gradient MLOps platform for simplifying notebook-to-deployment workflows.
Best For
Unique Features
- Gradient platform for ML workflows
VRAM
80GB
Architecture
Ampere
Tier
enterprise
Platform Features
Getting Started
Launching an NVIDIA A100 PCIe 80GB on Paperspace is straightforward via the Gradient platform. Sign up, create a notebook, select the GPU machine type, and start coding in JupyterLab with pre-installed ML frameworks. Per-second billing activates on launch, with easy scaling and pausing.
Steps
- 1Create a free Paperspace account at console.paperspace.com and add payment for Pro/Growth tier.
- 2Navigate to Gradient > Notebooks > New Notebook and choose a base image (e.g., PyTorch 2.1).
- 3Select 'A100-80GB' machine type, configure storage (up to 2TB NVMe), and set workspace region.
- 4Click 'Create' to launch; access via JupyterLab URL in 1-2 minutes.
- 5Install dependencies via terminal (e.g., pip install transformers) and verify GPU with nvidia-smi.
Pro Tips
- Leverage MIG mode for multi-user or parallel jobs: enable via nvidia-smi and partition into 7 instances for efficiency.
- Use Gradient Experiments for reproducible runs and auto-scaling deployments to production without code changes.
- Monitor costs with per-second billing—set idle timeouts and snapshot frequently to resume quickly.
Frequently Asked Questions
What is Paperspace's billing model for NVIDIA A100 PCIe 80GB?▾
Paperspace bills per-second for GPU instances including NVIDIA A100 PCIe 80GB. Per-second billing ensures you only pay for exactly the compute time you use, which is particularly cost-effective for short experiments, iterative development, and workloads with variable duration.
Does Paperspace offer spot instances for NVIDIA A100 PCIe 80GB?▾
No, Paperspace does not currently offer spot instances for NVIDIA A100 PCIe 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 Paperspace offer reserved instances for NVIDIA A100 PCIe 80GB?▾
Yes, Paperspace offers reserved instance pricing for NVIDIA A100 PCIe 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 Paperspace for current reserved pricing and commitment terms.
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