H100 Cost Insights and How It Compares with A100 and H200 GPUs

It’s no secret that artificial intelligence is reshaping industries, from healthcare diagnostics to autonomous driving and language models. Under the hood of this revolution? High-performance GPUs. According to NVIDIA’s Hopper architecture rollout in 2022, GPUs like the H100 have become the gold standard for training large-scale AI models.

CFOs and CTOs are increasingly asking, “What’s the H100 cost—and how does it compare to older GPUs like the A100 or newer ones like the H200?” This is especially true as businesses balance bare-metal server investments against cloud GPU hosting to meet enterprise-grade performance needs.

In this blog, we’ll dive into:

The real-world H100 cost

Comparisons with A100 and H200 GPUs

Cloud hosting vs on-prem TCO analyses

What drives the value for performance

And cost-efficient strategies for your AI infrastructure

The H100: A Performance Powerhouse with a Premium Tag

The NVIDIA H100 GPU, part of the Hopper series, delivers cutting-edge AI performance with H100 80 GB L2 cache, Tensor Core FP8 support, and HBM3 memory with 3 TB/s bandwidth 

Direct purchase price: ~$25,000 per card (some reports show prices pushed to $30k–$40k due to demand)

Resale listings: Often exceed $40,000 on secondary markets

Cloud providers rent out H100s with significant markup but offer flexibility:

Hourly rates range from ~$1.99 (RunPod) to ~$6.98 (Azure) per GPU

Example: Azure’s NC H100 v5 VM is $6.98/hr — about ₹575/hour

A100 vs. H100: Performance vs. Price

The A100, based on Ampere architecture, has been the backbone of AI since 2020. With each new generation, it has improved in Tensor Core performance and memory speed.

A100 MSRP: ~$10,000–$12,000

Cloud rent: ~$0.78/hr on platforms like Thunder Compute

Raw Compute Comparison:

H100 offers roughly 2× the FP performance of A100, and can train LLMs in half the time

Even though H100 costs ~2× as much per hour, it can reduce total training time — leveling the total spend

A practical example from MosaicML shows:

This illustrates greater cost-efficiency for H100 in intensive workloads 

Enter the H200: Next-Gen but Pricier

The newly released H200, based on Grace Hopper architecture, offers enhanced memory bandwidth and efficiency:

Price estimates are 30–50% higher than H100 — around $30k–$40k

Power-to-performance improvements could yield better TCO in specialized environments

For example, if an H100 costs $25k and H200 is 40% more, you’re looking at a ticket price near $35,000 per chip.

But unless your workload fully utilizes its added capabilities, it might be economically sound to stick with H100 — especially since independent comparisons suggest H100 remains the most balanced choice for most use cases .

Total Cost of Ownership: On-Prem vs Cloud

On-Premise (Bare Metal Server)

Suppose you buy 8 H100s ($200k) and install them in a DGX H100 rack system (~$400k total):

CapEx: ~$400,000

Annual colocation, power, cooling: ~$40,000/year (for 10 kW draw)

Depreciation: spread over useful life (3–5 years)

This breaks down to ~$88,000/year, excluding staffing and maintenance — significantly cheaper per year than cloud rentals if you're using GPUs extensively.

Cloud GPU Hosting

If you rent H100s on-demand:

At $6.98/hr from Azure: ~₹575/hr

24/7 use: $6.98 × 24 × 365 ≈ $61,200/year/GPU

You break even between cloud and purchase in less than a year if you're running GPUs full-time — echoing similar findings from TRG Data Centers 

Thus hybrid setups — baseline on-prem plus occasional cloudburst — can be optimal.

Key Takeaways for Prospective Buyers

H100 vs A100: Twice the price, but often half the training time = potential cost neutrality or savings

A100 remains viable: Great for lighter or mixed workloads where TCO matters

H200 is premium: Best for workloads that exploit its extra memory and bandwidth

Buy vs Rent: Buying and colocating H100s becomes cheaper than continuous cloud rent after ~8–12 months

Hybrid is best: On-prem for baseline jobs, cloud for spikes avoids overinvestment

Conclusion: Scale Strategically with GPU Infrastructure

In the AI arms race, GPU choice directly impacts both speed and budget. The H100 stands as a top-tier option — outperforming the A100 in speed and efficiency, and offering a more accessible entry point than the upcoming H200.

Breakdown:

H100 cost: $25k–$30k per unit; cloud rent $2–7/hr

A100: $10k–$12k; cloud ~$0.78/hr

H200: ~$30k–$40k; high-end use cases only

If your applications demand fast, efficient AI training and you can commit to high utilization, investing in H100s or hybrid setups makes financial sense. For smaller or bursty workloads, renting on GPU cloud hosting offers flexibility.

Whichever path you choose, calculating total cost — acquisition, power, depreciation, and scale — ensures you invest smartly in infrastructure that aligns with both performance goals and budget realities.