When NVIDIA unveiled the A100 GPU in 2020, it revolutionized deep learning, scientific computing, and high-performance workloads. Even in 2025, nearly five years later, the A100 remains one of the most widely used accelerators. Its balance of high memory bandwidth, CUDA cores, and Tensor-M individually-targeted dual implementations—has made it a go-to choice for researchers and enterprises.
Here's a compelling statistic: a recent survey from Lambda Labs shows over 45% of AI training clusters worldwide still rely on A100s, despite newer options like H100 and GH200 entering the market.²
But A100s don’t come cheap. The capital and operational costs can be staggering, especially when building large-scale clusters. Whether you're buying hardware, hosting GPUs in a data center, or running workloads in the cloud, understanding A100 GPU cost is crucial for ROI, budgeting, and future planning.
In this guide, we break down A100 pricing, compare acquisition options, factor in hidden costs, and explore cloud-hosted solutions—like Cyfuture Cloud's GPU infrastructure. By the end, you'll have a 360° view of the A100 investment landscape for AI and scientific workloads.
When launched, NVIDIA set the A100 MSRP between $11,000 and $13,000 for the 40 GB PCIe version. The 80 GB SXM version—with higher memory and bandwidth—was priced at $15,000–17,000. These prices included only the GPU chip itself, not the supporting hardware or infrastructure.
Due to ongoing demand and supply chain delays, recent pricing trends show a higher ceiling. You might see:
40 GB PCIe A100s: $12,000–14,000
80 GB SXM versions: $18,000–20,000
Used or refurbished A100s can be cheaper—$8,000–10,000—but carry risks like burn-in time, warranty limits, or prior performance under load.
Owning an A100 isn't just a matter of purchasing a chip. Here are the often overlooked costs:
A100 GPUs require robust hosts—either NVIDIA-certified servers or custom-built racks with high-wattage power supplies. Expect host-level costs in the $8,000–12,000 range for a dual-A100 machine, plus ~700W per GPU for power and cooling.
High-performance workloads typically need multi-GPU scaling. NVLink or InfiniBand networks add:
NVLink bridges: $500–700
IB switches and cables: $5,000–15,000
Each GPU server can draw several kilowatts, and racks require adequate power and cooling redundancy. Consider:
Rack cost: ₹200,000–400,000
PDU + cable runs: ₹50,000–100,000
Cooling automation and monitoring systems
NVIDIA premium support: ~10% of hardware cost/year
CUDA/X AI frameworks: often freely available
Local labor: system admins and data engineers required for cluster upkeep
Pros:
Full control and customization
No recurring compute charges
Predictable performance and latency
Cons:
High upfront CapEx and infrastructure costs
Physical maintenance and down-time risk
Scaling takes hardware procurement time
Pros:
Zero CapEx; pay-per-use
Rapid provisioning and scalability
Maintenance managed by provider
Cons:
Opex stacking with heavy use
Suited for burst or variable workloads
May lose GPU access during peak demand
Deployment Type |
Upfront Cost |
OPEX (Year 1) |
3-Year TCO |
On-Prem (1× A100) |
$13,000 (GPU) + $10k host & infra |
$3k maintenance + $5k power |
~$46k |
Cloud (Cyfuture ~₹500/hr) |
– |
₹500×24×365 ≈ $54k |
~$54k Opex |
Cloud + Hybrid (Burst) |
Purchase rental |
Mix of Opex & CapEx |
Optimized mix |
If you're running AI workloads non-stop, buying and hosting makes sense. For periodic training jobs, the cloud is more cost-effective. Many AI teams use hybrid architectures to optimize GPU use without overspending.
Regardless of deployment model, here are four strategies to reduce overall A100 GPU cost:
Leverage Spot/Preemptible Instances: Access A100 use at discounts via AWS spot instances or similar models in platforms like Cyfuture Cloud.
Reserve Instances or Commitments: Reduce hourly rates by locking in long-term usage.
Monitor GPU Utilization: Use tools to prevent idle time and maximize utilization.
Cluster Sharing: Let teams pool GPU resources to increase overall efficiency, reducing idle assets.
When evaluating GPU infrastructure solutions, here’s what to consider:
Transparent pricing: Cost per GPU/hour
Support level and uptime guarantees
Scalability & elasticity of the platform
Integration options: Kubernetes/GPU scheduling, container support
Geographic reach and compliance
Cyfuture Cloud stands out for hybrid GPU hosting: offering A100s with flexible usage and local data centers to fulfill Indian business needs.
Whether you're training complex models or running simulations, A100 GPUs are proven performers. But their price, maintenance, and scaling complexity means you can't just choose hardware blindly.
Key takeaways:
A100 MSRP in 2025 ranges $12–20k; used units are cheaper, but conditional
Total Cost of Ownership far exceeds the GPU—factor in infrastructure, power, networking, and labor
Cloud-hosted A100s eliminate large upfront costs and offer flexibility—but at higher per-hour rates
Use a hybrid approach to optimize both cost and performance
If you're building AI infrastructure for the first time or seeking to scale existing GPU clusters, I can help model your ROI using on-prem setups or cloud platforms like Cyfuture Cloud. Interested in a free TCO consultation? Happy to help you design the most effective roadmap for your workloads.
Let’s talk about the future, and make it happen!
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